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Site Profile: Merseyside
Population: 1,381,200 (9th)
645 km2 (43rd)
5 Boroughs – Knowsley, Liverpool,
Sefton, St Helens, Wirral
Valued at £21.9 Billion (2.1% of U.K
economy) GVA (Gross Value Added)
0 1 2 3 4 50.5
Miles
Ward_Boundaries
No. of Households With Access To A Vehicles
18 - 94
95 - 171
172 - 247
248 - 323
324 - 400
401 - 476
477 - 552
553 - 628
629 - 705
706 - 781
No Data
¡
No. Of Households with
No Access To A Vehicle
(Inset of Wirral and Liverpool Boundaries)
0 2.5 5 7.5 101.25
Miles
0 1 2 3 4 50.5
Miles
Ward_Boundaries
No. Of Households with Dependents
8 - 28
29 - 49
50 - 69
70 - 89
90 - 110
111 - 130
131 - 150
151 - 170
171 - 191
192 - 211
No Data
¡
No. Of Households with
Dependents in Merseyside
(Inset of Wirral and Liverpool Boundaries)
0 2.5 5 7.5 101.25
Miles
The
Maps:
The
Maths:
PC = TC
TC + TT
CIJ = a1.tIV + a2.tWK + a3.tWT
+ a4.tIN + a5.F + a6
The
Method:
Ai = Σ [BJf(CIJ)]
Accessibility To Destination:
This will demonstrate how
accessible a location is based
on either Thiessen polygons
(zone inside polygon is closer
to that sample) or isochrones
(coloured bars of equal value).
Modal Split/Destination
Attractiveness:
Multi-nomial logit models
calculating modal split.
(Equation can be used for
destinations). These can be
portrayed 3D or by colour
(large spike, more accessible)
Route Allocation/ Trip
Chaining:
Network Analyses using the cost
equation above will show the
“cheapest” route for an
i n d i v i d u a l t o u s e . A l s o
demonstrates trip-chaining (best
way to carry out multiple tasks).
D e m o n s t r a t e a n d
evaluate disaggregate
t e c h n i q u e s o f
accessibility analysis
Potential Issues
Establishing parameters. How to
measure a preference?
How disaggregate is too
disaggregate?
Issues with deterrence functions.
How viable are the methods?
Literature Review:
1970
Geographical Space Accessible Space
2011
Hagerstrand developed a
time-geographies concept.
3 M a i n C o n s t r a i n t s :
“capability constraints”,
“coupling constraints” and
“authority constraints”.
Computing power
h a s a l l o w e d f o r
disaggregate activity
modelling to be
c a r r i e d o u t .
S t i l l a “ p i o n e e r i n g ”
m e t h o d . B u f f e r s a r e
predominant technique
Not a spatially defined problem. – Large
geographical space, small accessible space.
Need to consider area mobility, individual
m o b i l i t y a n d a r e a a c c e s s i b i l i t y.
Economic status, car availability and physical
or social preferences and limitations can
affect how a person can or wants to travel.
A Local Travel Plan (LTP) set up
by the Local Authority and
Merseytravel seeks to “develop a
fully integrated and sustainable
transport network... And ensures
good access for all in the
community” (Merseytravel 2000)
The End Result:
GIS outputs of varying types with two main aims:
1. Levels of accessibility for individuals demonstrating how their lifestyles
affect their travel choices and access.
2. Differences between the disaggregate methods used in the study and
aggregate methods carried out as a comparative tool.
This will establish if the individual level studies are more accurate than
aggregate measures, and if so, to what extent, with what outcomes?
What Next…?
Need to establish parameters to use within the functions.
Carry out the GIS techniques and modelling.
Analyse and evaluate the datasets – See what is possible.
Large number of citizens in
certain areas are not car
users and as such find it hard
to carry out fundamental
tasks, such as taking children
to school or going shopping.
Thus creating and social
exclusion transport poverty.
f f
Some Important Questions:
Can GIS model the movements of individuals
based on their preferences, motivations and
restrictions, and how these relate to transport?
Does a micro-level analysis offer a viable
alternative to the current methods of study?
One Size Doesn’t Fit All...
Many relationships exist between
different individuals preferences,
capabilities and the levels of their
t r a n s p o r t a c c e s s i b i l i t y .
Aggregate methods (groups, not
individuals) can miss important
elements of a persons accessibility.
Study Aims:
Carry out an accessibility study of Merseyside at a disaggregate
level.
Highlight inefficiencies
in aggregate modelling
Show that individuals
from the same areas
have differing travel
patterns.](https://image.slidesharecdn.com/allall-140515044924-phpapp01/75/Masters-Dissertation-Posters-2014-25-2048.jpg)
![• Road pricing (tolling) dates back to the 17th century introduced
after the turnpike Act – 1663 in UK and to 18th century in the USA;
• Was predominantly used to raise funds for construction and
maintenance of highways;
• In recent times, tolling has been used for numerous reasons;
• Implemented in Singapore since 1975 as a traffic management
tool;
• In London, it has been used since 2003 to reduce congestion and
protect the environment;
• Norway, Sweden and Malaysia use road tolling to raise funds for
road transport budget support.
Road Pricing: A Case of Competing Private Road Toll Operators
The study intends to:
• Study techniques of identifying Nash Equilibrium for multiple toll
operators;
• Examine toll levels that ensure private operators maximise
revenue and meet the Nash Equilibrium conditions;
• Establish how revenue maximising tolls compare with social
welfare maximising tolls.
By Jonah Mumbya | Supervisor – Mr. Andrew Koh | Second Reader – Dr. Chandra Balijepalli
Introduction
Objectives
Motivation
Effects of Increasing Congestion Environmental Costs of Traffic Government Budget Constraints
• Global costs of congestion are high and
projected to increase with increasing
traffic delays;
• In UK, congestion costs (due to delay)
stood at £20bn in 2000 and projected to
increase to £30bn by 2020;
• NTM projects traffic to grow by 43% as a
result of a 66% GDP growth from 2010-
2040 in England alone;
• This would lead to congestion increasing
by 114% and lost seconds per mile would
increase by 36% hence cost as travel
speeds would reduce by 8%.
• Transport is the third largest
contributor to global warming
just behind energy (electricity
and heating) and industry;
• In UK, Road transport
contributed over 27% to
Green House Gasses with
cars having 58% of this in
2009;
• With increasing motorisation,
this is likely to be the same or
worse with time.
• Governments are continually
getting constrained to finance
road infrastructure using
traditional budget
appropriations;
• Hence road users ought to
meet part of the road
infrastructure investment
costs/budgets;
• Road pricing supports about
32% of Norway’s national road
system budget and 46% of
Spain's road budget.
Methodology
a) Link Selection;
Link selection shall be based on the difference
between link marginal cost and average cost and the
level of congestion of the do-nothing scenario.
b) Determine Tolls;
Iteratively, tolls will be set until a Nash Equilibrium is
achieved for the competing toll operators.
c) Traffic Assignment;
Using SATURN, traffic shall be assigned to the
network based on Wardrop’s first equilibrium
principle.
d) Calculation of Revenue and Benefits.
Based on assigned link flows from SATURN,
revenues and social benefits will be calculated.
Test Network – Edinburgh
1
2
3
4 5
Select Links
[SATURN]
Set Tolls
Assign Traffic to
the Network
[SATURN]
Is Nash
Equilibrium
Achieved?
Calculate
Revenue and
Social
Benefits
No
Yes](https://image.slidesharecdn.com/allall-140515044924-phpapp01/75/Masters-Dissertation-Posters-2014-27-2048.jpg)
![INVESTMENT DECISIONS FOR RESILIENT TRANSPORT INFRASTRUCTURE:
A CASE STUDY OF THE DAWLISH RAILWAY LINE COLLAPSE
Kwame Nimako: MSc Transport Planning and Engineering (2013-2014) Supervisors: Prof. Greg Marsden and Prof. Nigel Wright
1. BACKGROUND
• A good transport system promotes the movement of people,
goods and services from one point to another under normal
conditions (Amdal and Swigart, 2010). A nation’s economic
vitality to a large extent depends on its transport network
(Amdal and Swigart, 2010).
• The occurrences of natural disasters such as flooding, make
transport networks such as railway lines vulnerable (Doll et
al., 2013), thereby impacting negatively on train services.
• For the disruption at Dawlish, the Train Operating Companies
will be paid £16 million by Network Rail for lost revenue over
the period (BBC, 2014).
• As the frequency and magnitude of such disruptive events
become more probable in future due to climate change, the
cost of providing engineering interventions required for
reliable transport services increases significantly.
• Since most transport infrastructure are long term assets (Doll
et al., 2013), there is the need for adequate investment
decisions on cost effective strategies to be employed to
enhance their resilience over their life span.
2. AIM
The aim of this dissertation is to develop a methodology to be
utilised in making cost-effective investment decisions to
improve the resilience of railway lines to disruptions.
3. OBJECTIVES
To achieve this aim, the following objectives have been set:
i. Understanding how demand for transport changes during
a major flooding event
ii. Estimating the impacts of the resultant disruption on
users of the infrastructure
iii. Collecting estimates of alternative flood risk mitigation
investments
iv. Developing a methodology to assess the cost-
effectiveness of such investments under different future
scenarios of flood risk
Great Western Rail line - London-Exeter-Dawlish-Plymouth-Penzance. (Source: First Great Western network map)
Location of Dawlish and the Railway line Disruption (Source: Google.com)
4. PROPOSED METHODOLOGY
5. EXPECTED OUTCOME
It is anticipated that this study will produce an Investment-
Frequency Matrix based on current and future scenarios of
disruptions to be utilised to improve the resilience of railway
lines.
6. REFERENCES
• Amdal, J.R. and Swigart, S.L. 2010. Resilient Transportation Systems
in a Post-Disaster Environment: A Case Study of Opportunities
Realized and Missed in the Greater New Orleans Region, 2010.
• Doll, C. et al. 2013. Adapting rail and road networks to weather
extremes: case studies for southern Germany and Austria. Natural
Hazards. pp.1-23.
• British Broadcasting Corporation. 2014. Storm-hit Dawlish rail line
compensation payout revealed. [Online]. [Accessed 28 April 2014].
Available from:http://www.bbc.co.uk/news/uk-england-devon-
27055780.](https://image.slidesharecdn.com/allall-140515044924-phpapp01/75/Masters-Dissertation-Posters-2014-28-2048.jpg)
![Understanding travel behaviour to stadium and arena events :
A Cardiff case studyLaura Crank, MSc Transport Planning (FT)
Supervisor: Bryan Matthews
The importance of travelling to
events
The car is the most popular mode of transport to
stadium and arena events, and as the attendance of
such events increases, it is noted, “…the demand for
travel is heavily constrained both in time and space”
(Robbins et al. 2007:303). High demand in a short space
of time leads to congestion on the roads and
overcrowding on public transport.
Providing for such temporary ‘peak’ crowds would leave
the additional infrastructure and services underused for
the most part, which is economically unviable
(ibid:304).
The story so far…
Obtained at least 150 responses between face-to-face
and online surveys
Met with Cardiff Council’s Operations Manger of Major
Projects (Infrastructure) at one of the venues during
an event. Managed to discuss issues at hand and how
the council managed the city during large events
Methodology
Design survey taking into account aims and
objectives. Test survey to ensure it is
understandable and quick to complete
Sample a range of events to capture different socio-
economic groups
Conduct face-to-face surveys at both Millennium
Stadium and Motorpoint Arena, and promote online
survey to gain 200+ responses
Differentiate between results for each event,
establish differences in audiences, distances
travelled, travel habits
Conclude what would need to be done to encourage
modal shift to public transport
Aims and Objectives
To understand travel behaviour to events, developing on
previous research at other stadiums and arenas
It is becoming a
growing interest to
understand the
behaviour of
spectators and to
determine what
changes would have
to occur to the
public transport
system to increase
attractiveness and
modal share of
public transport to
events.
Research Questions
What is the percentage of different modes of transport
used to access events in Cardiff?
What factors influence mode choice to events in Cardiff?
Are there differences between mode choices to the two
venues?
Are different event audiences more “sustainable” in
their travel choices, or willing to change their event
travel habits?
What changes would have to be made to public transport
to increase its usage during events in Cardiff?
References
Robbins, D. et al. 2007. Planning Transport with Special Events: A Conceptual
Framework and Future Agenda for Research. International Journal of Tourism
Research. 9. Pp. 303-314.
Yeates, J. et al. 2009. Changing Travel Patterns of Arena Visitors:
Transportation Demand Management for Urban Arenas and Stadia. [Online]
Available at:
http://www.ite.org/Membersonly/techconference/2009/CB09C3001.pdf
[Accessed April 2014] Washington: Institute of Transport Engineers. Modal split before and after TDM measures
Location of venues and public
transport stations in the vicinity of
the Cardiff region
A survey of stadium travel in
New York found that after
transport demand measures
(TDM) were implemented,
there was a decrease in car
travel to events, whilst
public transport use
increased.](https://image.slidesharecdn.com/allall-140515044924-phpapp01/75/Masters-Dissertation-Posters-2014-31-2048.jpg)
![www.kliaekspres.com www.ktmkomuter.com.my www.ktmb.com.my www.prasarana.com.my www.spad.gov.my
• Population ~29m
• Capital: Kuala Lumpur
• GDP per capita: ~10k USD
• Age group [16-64]
expandingKuala Lumpur Metropolitan Area (KLMA)
www.wikimedia.com
• Rapid motorization rate – Car Ownership at ~320 cars / 1000 per.
• Fuel subsidized – Oil producing country
- Approved Price / Floating Price Mechanism : Pump price
lower/equal than actual market value.
• Train Services: Intercity and Urban (KLMA) available
Components Description
Key Question
Deduction of constant elasticity
Method:
Urban: Trips Made
Intercity: Kilometer-Passenger
Urban: Trips Made
Intercity: Kilometer-Passenger
Online Survey - Considering type of trip, gender, income, location.
Dispersion question:
Given a situation is that the fuel price has gone very high and is no
longer affordable. If you had to make a leisure trip (visiting
family/friends, holiday etc) . If petrol prices are too high, how do
you travel?
A) Train B)Bus
Key Question:
Deduction of the cross elasticity of rail demand with respect of
pump car-fuel price.
Area and Mode
Area of study will be in Malaysia (East and West). Related modes:
Train and Cars.
Impact of Car Petrol Prices on Rail Demand: The Case of Malaysia
Nik Mohd Rafiq Bin Wan Ibrahim
MSc. (Eng.) Transport Planning and Engineering
ts13nmrb@leeds.ac.uk
Supervisor:
Prof. Mark Wardman
Institute for Transport, University of Leeds
Quick Overview:
Deduction of the cross-elasticity of rail passenger
demand in Malaysia with respects to car petrol prices.
TrainCar
rainRidershipT
arRidershipC
iceCarFuelCariceCarFuelRail
V
V
,Pr,Pr, ||
1. Situation Appraisal 3. Scope 5. Methodology / Data Collection
4. Literature2. Motivation
iceCarFuelRail Pr,
iceCarFuelCar Pr,
rainRidershipTV
arRidershipCV
TrainCar,
Urban Rail
(Kuala Lumpur
Metropolitan Area)
Nationwide
Intercity Rail (Nationwide)
• Interesting to see in a fuel-subsidized country, where the
actual transport fuel cost is not paid by the user, would
there be any significant mode shift.
• As oppose to EU or UK, whereby taxation of fuel is a
national income stream, the effects of increase modal-
shift to trains (public transport in general) in countries
where fuel are “costly” to the nation, would be a point of
interest.
• Trending projects to improve in urban rail especially in
KLMA and intercity rail services (Double Track, HSR)
• Subsidy will be removed?
Acutt and Dodgson 1996, Cross elasticities of demand for
travel, Transport Policy Vol 2, pp. 271-277
Train Type Year Cross Elasticity w.r.t car-fuel
price
InterCity, NSE,
Regional
1992/93 0.041 – 0.094
Underground 1992/93 0.017
Train Type Year Cross Elasticity w.r.t car-cost
Inter Urban - 0.59
Urban - 0.25
Paullney et al 2006, The demand for public transport: The
effects of fares. Quality service and car ownership,
Transport Policy, 13(4), pp. 295-306
40.0
50.0
60.0
70.0
80.0
90.0
100.0
110.0
120.0
130.0
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Consumer Price Index
(2000 = 100) (Source
DOSM)
0
2000
4000
6000
8000
10000
12000
14000
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Retail Fuel Sold [million
liters] (RON97 + RON95)
(Source KPDNKK)
0
20000000
40000000
60000000
80000000
100000000
120000000
140000000
160000000
180000000
200000000
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2001
2003
2005
2007
2009
2011
Passenger Ridership Urban
Rail (Source: EPU)
0
200000000
400000000
600000000
800000000
1E+09
1.2E+09
1.4E+09
1.6E+09
1.8E+09
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Railways, passengers
carried (passenger-km)
[Source: DOSM]
MALAYSIA
Objective:
To investigate the relationship of car fuel price to rail demand.
Increasing of car fuel price (either due to global market or
government’s removal of the subsidy) should hypothetically
increase train patronage.
1. Literature Review on global PED
2. On-line Survey: Stated Preference
3. Regression from existing data:
Cross elasticity is calculated using an equation that consists of several components.
These components are partly review from literature to compare other
international values followed by a re-calculation using method described below. A
crucial part of this study is an online survey to deduce the diversion factor; non-
payable option of cost car-fuel is hypothesized.
(Source: DOSM)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1980
1984
1988
1992
1996
2000
2004
2008
2012
Pump
price for
gasoline
(US$ per
liter)
(Source:
World
Bank)
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
0
500
1000
1500
2000
2500
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Length of railway track (km)
(Source: DOSM)
Roads, total network (km)
(Source: World Bank)
Track(km)
RoadNetwork(km)
-
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
1980
1986
1992
1998
2004
2010
Cars
Motorcycle
Other Vehicles
Goods Vehicle
Bus
Taxi and Private
Hire
Mileage travelled
Deduction from Retails Fuel Sold –
considering fractioning to cars and yearly
fuel consumption, calibrated with
VehicleKilometerTravelled (recent Survey)
Pump Fuel Price
Considering
Consumer Price
Index
Registered Vehicles over time](https://image.slidesharecdn.com/allall-140515044924-phpapp01/75/Masters-Dissertation-Posters-2014-36-2048.jpg)
![• The construction of roads could be traced
to 312 BC when Romans built roads to
connect major cities within their empire.
These roads were regularly maintained.
• Pavement rehabilitation and construction
are very expensive and do not create room
for trials.
• Pavement evaluation provides information
to ascertain the type of maintenance
activity to be carried out.
An Examination and Application of Flexible Road Pavement Evaluation Methods:
A review of the methods and application to roads in Sierra Leone
1. Background
2. Objectives
• To identify relationships between the
evaluation methods of flexible pavement.
• To locate and determine the seriousness of
the pavement distresses on the selected
roads.
• To determine possible structural surveys
and/or laboratory tests to be carried out.
• To ascertain the ‘present serviceability
rating (PSR)’ and the ‘pavement condition
score (PCS)’ for the selected roads.
Transverse Crack Longitudinal Crack
RuttingPatches
Alligator Cracking
Pothole
7. Methodology
Pavement Distresses on roads
• The present serviceability index would give an indication
of the ride quality of the road.
• The present condition score would indicate the extent
and severity of the distresses on the road.
4. Why determine ‘PSR’ and ‘PCS’ ?
5. Selected Roads
By Serrie Henry Willoughby MSc(Eng) Transport Planning and Engineering Mr. David Rockliff (Supervisor), Prof. Anthony Whiteing (2nd Reader )
• A comparison between the UK methods and that
specified in the oversees ‘Road Note 18’ for tropical
countries would be considered in this work.
• The PSR would be obtained from highways engineers,
commercial and private drivers.
3. Scope
The relationship between the different types of evaluation
method shall be obtained by review of literature.
The positions of distresses shall be obtained by the use of a
hand held GPS device and wheel meter.
The pavement serviceability index shall be assessed by
driving through the road and rating the ride quality on a scale
of 0 – 5: were ‘0’ would indicate very poor and ‘5’ would mean
very good.
The pavement condition rating would be obtained by noting
the extent and severity of distresses and calculating the
index using the equations in the FHWD manual as outlined
below:
PCR = 100 - [(100 - AC_INDEX) + (100 - LC_INDEX) + (100 - TC_INDEX) +
(100 -PATCH_INDEX) + (100 - RUT_INDEX)]
• Scale:
POOR (<=60), FAIR (61 - 84), GOOD (85 - 94) , EXCELLENT (95 - 100
The selected roads are Old railway
line , Pademba road and Benjamin
lane in Freetown, Sierra Leone.
Original Pavement Design
and Construction Records
Analysis
Establish Probable
Causes of Pavement
DistressesObserved Pavement
Distresses
Field Tests
Surface Condition
Roughness
Deflection
Frictional Resistance
Field Samples
Laboratory
Evaluation
Rehabilitation
Alternatives Based
on Pavement Design
Principles
Background
Maintenance
Performance
Geometrics
Environment
Traffic
Economics
Thin/ Thick Overlay
Inlay
Recycling
Reconstruction
Pavement
Evaluation
PerformanceCondition Safety
Structural Material
6. Evaluation for Rehabilitation
Location Referencing](https://image.slidesharecdn.com/allall-140515044924-phpapp01/75/Masters-Dissertation-Posters-2014-43-2048.jpg)
![ECONOMY ENVIRONMENT
+ Reducing the cost of import fossil fuels
+ Gaining profit by export biofuels
+ Reuse abandoned agricultural land
− Low energy density of biofuels, lead to more
distances, travel times and labour costs.
− Feedstock processing costs
+ Less GHG emission
+ Carbon negative renewable energy
− Extra trips/distances are needed for collecting
feedstock cause greater air pollution
− The changes of Land use cause emissions
− Decrease biodiversity
− Decrease soil, water quality
SOCIETY ENERGY SECURITY
+ Increase employments
+ The fuel prices become more reasonable
− Food security concerns
− Poor quality of biofuels decrease the vehicles
performances
− The hi-tech vehicles are not affordable for the
whole public, increase the inequality.
+ As an alternative energy source to enhance
energy security
− The failure of implement biofuels can weaken
energy security
− Hard to forecast if biofuels face the same
situation like oil crisis in the future
“4ASPECTS”OFIMPACTSAIMS&OBJECTIVESMETHODOLOGY
Rose I-Hsuan Lien (ts13ihl@leeds.ac.uk), Supervised by Dr Anthony Whiteing
iofuel’s issue used to focus on its energy efficiency and the food crisis it
might cause. However, how to optimise the supply chain still lack of
discussion at present. To illustrate, negative impacts of biofuel supply
chain not only need to be recognised but be solved through sustainable
ways. The main missions of this supply chain management are to
ensure the costs are competitive and the supplies are continuous (Gold
and Seuring, 2011; Hess et al., 2007; Sims and Venturi, 2004). The
demands of biofuel in Taiwan are increasing. Since the usages of
biofuels in Taiwan are still developing, it is an advantage for Taiwan to
implement the sustainable strategies into the supply chain.
Harvest & collect
·Feedstock type
·Harvest frequency
·Topography
·Post-harvest process
place (bail and chip)
Storage
·Demands & supplies
·Storage facilities
·At farm or storage
terminal
·Degradation & dry
matter loss
Pre-treatment
· Pre-treatment with
storage
· Mode: drying,
pelletisation
Energy conversion
· Place for refine and
blend
Gas station
· The facilities to
store biofuels
Consumer
· Vehicle
performance
· Vehicle choices
Transport:Modechoice,law&infrastructure,vehiclescapacity
Reference: ①Green Energy Industry Information Net, (2014). Introduction of biofuels Industry. Taiwangreenenergy.org.tw. Available at: http://www.taiwangreenenergy.org.tw/Domain/domain-4.aspx
②Hess, J.R., Wright, C.T., Kenney, K.L., 2007. Cellulosic biomass feedstocks and logistics for ethanol production. Biofuels, Bioproducts and Biorefining 1 (3), 181e190. ③Gold, S. and Seuring, S. 2011.
Supply chain and logistics issues of bio-energy production. Journal of Cleaner Production, 19 (1), pp. 32--42. ④IEA. 2013. Tracking Clean Energy Progress 2013. [e-book] Paris: Internation Energy
Agency. Available through: Internation Energy Agency http://www.iea.org/publications/TCEP_web.pdf. ⑤Sims, R.E.H., Venturi, P., 2004. All-year-round harvesting of short rotation coppice eucalyptus
compared with the delivered costs of biomass from more conven- tional short season, harvesting systems. Biomass and Bioenergy 26 (1), 27e37.
B
1. Case studies:
Studying biofuel promoting schemes in developed/emerging/developing countries to compare and
organise the implementations under different circumstances.
2. Interviews:
To apply potential options to Taiwan, the locals’ opinions and experiences are important. Thus the options
will be provided to experts in different areas - academic, planner, government, sustainable workers in
Taiwan, and interview them to know the attitudes and possibilities to implement these options.
PROBLEM → APPROACH → SOLUTION → ADAPTION → APPLICATION
Understand
biofuels
implementation
Understand
operation
processes
Develop sustainable
Instruments
Develop
options for
Taiwan
Summarise the feasibilities of
options
①Policies&
infrastructures
②Impacts in “4
aspects”
③Future goals &
trends
①”Who” drive
&“who” are
involved
② Operation
processes
③Differences
between
nations
①Instruments integration
②Factors of instrument
choices
③Underlying barriers and
conflicts
④Strengths &
weaknesses in “4
aspects”
①Develop
options
②Stakeholders’
viewpoints
①The most and the least
feasible options
②Influents of choices
③Conflicts of influents
between different
stakeholders
④Improve options:
negative↓, positive↑
(Gold and Seuring, 2011)
Case in Taiwan:
Bioethanol:
(Import only, no local factories (high investment risk))
•07/2009:Taipei&Kaohsiung Metro Area Ethanol
Promotion Project, total 14 gas stations supply E3.
•2012: 210 kilolitres used.
Biodiesel:
(Imported palm oils and local cooking waste oils)
4 stages implement plan:
1.11/2006-06/2008: Green Bus Project, encouraged
buses use B1-B5 biodiesel
2.07/2007-06/2008: Green County Project (B1), in two
counties, the oil companies in these areas were
chosen to blend biodiesels and to sell in the gas
station there. B100 biodiesel were refined by Taiwan
factories , also energy crops were allocated to plant in
fallow farmlands.
3.07/2008: Full implement B1
4.06/2010: Full implement B2
• 2012: 100,000 kilolitres used.
• 2014: Poor quality of biodiesels influenced vehicles
performance, government plan to pause B2 projects
in June.
• 2016: Full implement B5, estimated 250,000 kilolitres
biodiesel required.
(Green Energy Industry Information Net, 2014)
OPERATION ISSUES
Global biofuel productions projections and target.
(Source: IEA, 2013)](https://image.slidesharecdn.com/allall-140515044924-phpapp01/75/Masters-Dissertation-Posters-2014-50-2048.jpg)
![The Importance of Eco-Driving
In the last decades, engine technology and vehicle performance has improved, while most
drivers have not adapted their driving style.1 Eco-driving represents a driving culture which
suits modern engines and makes best use of advanced vehicle technologies, such as
maintaining a steady speed at low rpm, shifting gear early and rolling in neutral. The benefits
of adopting such behaviour include;
Lower greenhouse gas emissions
It is widely accepted that greenhouse gas emissions contribute to climate change. Eco-driving
can be embraced as a simple and effective environmentally friendly initiative that can lower
vehicle emissions by 20g per kilometre.2
Save cost on fuel
By adopting eco-driving practices, fuel efficiency can be improved by 15% or more lowering
the demand for fuel.3
Source (Fiat, 2010, p. 25)
Eco-driving: who does it and why ?
Identifying the motivational factors
References
1 Eco Drive (2014). What is eco-driving. [Online]. [Accessed 7th April 2014]. Available at: http://www.ecodrive.org/en/what_is_ecodriving-/
2 Fiat. (2010). Eco-driving Uncovered. [Online]. Accessed 7th April 2014]. Available at:
http://www.fiat.co.uk/uploadedFiles/Fiatcouk/Stand_Alone_Sites/EcoDrive2010/ECO-DRIVING_UNCOVERED_full_report_2010_UK.pdf
3 Baltutis, J. (2010). Benefits of Eco-Driving. [Online]. (Accessed 7th April 2014]. Available at:
http://www.unep.org/transport/PDFs/Ecodriving/Ecodriving_pwpt.pdf
4 Mensing, F et al. (2014). Eco-driving: An economic or ecologic driving style? Transportation Research Part C, 38, 110 – 121.
5 Powell, D. (2008). Extreme Driver – with a difference. New Scientist. 200, pp. 42 -43
6 DfT. (2011). Eco-driving: Factors that determine post test take up. [Online]. [Accessed 7th April 2014]. Available at:
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/142536/Eco_safe_driving.pdf
7 Delhomme, P et al. (2013). Self-reported frequency and perceived difficulty of adopting eco-friendly driving behaviour according to gender, age
and environmental concern. Transportation Research Part D, 20. pp. 55 – 58.
Savings per
car life cycle
Fuel
Consumption
CO 2
Emissions
Savings
Average
Driver
-6% -1,088kg £480
Top 10% -16% -2,895kg £1,260
Literature Review
Motivational factors
- Economic 4
- Environmental concern 4
- Embracing the challenge 5
- Road safety 6
Barriers to adoption
- Perceived difficulty 7
- Lack of awareness 6
- Technological innovation is more interesting 2
- Lack of tuition in driving schools 6
Group most likely to eco-drive
- Middle aged females with a high environmental concern 7
Group least likely to eco-drive
- Young males and females 7
Michael Wilson
Supervisor: Dr Daryl Hibberd
Institute for
Transport Studies
Methodology
Step 1 – Develop an online scenario based questionnaire divided into 4 sections
Section 1 – Personal Characteristics:
Identify the key variables such as age, gender,
income and environmental concern.
Section 2 – Strategic Decision Scenarios
Examine the motivation for the selection of vehicle type,
checking tyre pressure and fuel choice.
Section 3 – Tactical Decision Scenarios
Examine the motivation for route choice
regarding congestion , road terrain and whether
individuals consider removing excess vehicle weight.
Section 4 – Operational Decision Scenarios
Examine the motivation for driver speed, driving
style, air conditioning use and idling.
Example Question
You are given the choice of two routes to the same
destination. Route 1 will take 1 hour and emit 5% more CO2.
Route 2 will take 10 minutes longer, emitting 5% less CO2.
Which do you choose?
Step 2 – Obtain 100+ respondents to achieve a representative sample of the
population. Respondents will be private vehicle users with a valid
driving license.
Step 3 – Analyse results by performing a regression analysis between variables in SPSS to
answer research questions.
Possible Further Research
Examine how eco-driving campaigns can target driver groups through motivational
factors and removal of barriers
Attempt to quantify the environmental impact if eco-driving was implemented as a soft
policy measurement
Undertake a cost-benefit analysis to calculate the NPV to the driver of eco-driving
Aims and Objectives
The aim of this project is to explore the relationship between individual characteristics, eco-
driving behaviours and motivational factors. The following objectives have been set to
answer the overarching question of who eco-drives and why?
Identify motivations behind eco-driving
Identify which drivers are most likely to undertake eco-driving behaviour
Identify whether individuals are conscious of eco-driving
Identify the potential barriers to eco-driving
Hypotheses
The following hypotheses will be tested in order to provide a structure and focus to answer
the project objectives.
The strongest motivation to eco-drive is fuel conservation
Lower income groups are more likely to eco-drive because of economic influence
Individuals who travel a greater distance are more likely to eco-drive because of the
experience they have gained
The greatest barrier is awareness of eco-driving practices](https://image.slidesharecdn.com/allall-140515044924-phpapp01/75/Masters-Dissertation-Posters-2014-57-2048.jpg)
Uploaded byInstitute for Transport Studies (ITS)
Masters Dissertation Posters 2014
This document presents various studies focused on transport demand and vulnerability, including the development of a transferable car travel demand model using data from Nairobi and Dar-es-Salaam, methodologies for improving flood-risk transport networks in the UK, and an analysis of passengers' willingness to access regional airports. Each study aims to enhance understanding of transport behaviors, model transferability, and emergency planning in the face of pressing environmental and infrastructural challenges. The findings emphasize the importance of adaptable models and strategic cooperation among transport facilities to improve accessibility and emergency response.
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Examines the development of a car travel demand model in Nairobi and Dar-es-Salaam, focusing on household car ownership, trip generation, and modeling performance.
Evaluates transport network flood-risk vulnerability; aims to identify weaknesses in flood-prone areas and improve emergency strategies.
Investigates factors influencing departure airport choice related to surface transport access for passengers traveling between Manchester and Leeds.
Analyzes reflective cracking in asphalt pavements, exploring causes and solutions to improve durability and maintenance strategies.
Examines transport challenges in Jakarta, including congestion impacts, socio-economic factors, and recommendations for transport policy improvements.
Describes accident characteristics on the Attiki Odos motorway in Greece and aims to model queue dynamics and predict congestion.
Explores barriers to e-bike adoption among cyclists, emphasizing the need for awareness and policy support to boost cycling uptake.
Investigates traffic congestion's impact on fuel consumption and emissions in Headingley, focusing on real-world vehicle data analysis.
Analyzes factors influencing the UK’s CO2 reduction focus in transport, assessing implications for policy and climate change.
Explores the integration of mobile phone and demographic data to improve trip generation model reliability in transport planning.
Investigates the implications of concessionary travel scheme in the UK, assessing changes in bus service dynamics and user impacts.
Analyzes transportation challenges in Landlocked Developing Countries, focusing on freight transport efficiency in Uganda.
Examines factors contributing to car dependency in Leeds, discussing infrastructure, cultural attitudes, and potential policy changes.
Discusses the state of road maintenance in Britain, highlighting investment needs and public satisfaction metrics for funding decisions.
Evaluates the impact of active and public transport facilities on commuter behavior concerning car use, focusing on workplace design.
Investigates emissions from road transport in Ghana, assessing air quality and health implications for communities near busy routes.
Explores how improvements along Leeds' canal towpath can encourage cycling, assessing safety perceptions and user behavior.
Analyzes the role and risks of private financing in airport expansions, with a case study on Manchester Airport’s growth plans.
Examines how social media influences organization of protests affecting transport networks, exploring implications for urban mobility.
Discusses NMT Master Plan benefits within Ghana, focusing on infrastructure, congestion, and challenges in Accra and Tema.
Analyzes economic impacts of enforcing axle load control on transit trade and road investments along West Africa Highway.
Examines determinants of transport mode choice for shopping trips in Great Britain, looking into accessibility and policy implications.
Investigates elasticity variations in passenger rail demand in the UK, focusing on fare price sensitivity and demand drivers.
Explores income's impact on welfare measures in transport investment appraisal using discrete choice models.
Assesses accessibility levels and their influence on customer satisfaction for transport plans in Merseyside.
Investigates the impact of eco-driving behaviors on emissions and fuel savings, assessing motivations and barriers.
Examines how stated preference survey designs impact valuation of soft factors in transport demand modeling.
Compares car ownership characteristics between UK and Japan, focusing on socio-economic influences on household decisions.
Analyzes the relationship between transport infrastructure investment and its effects on economic growth and employment.
Assesses the impact of ghost island provisions on capacity and safety at priority T-junctions in local road networks.
Analyzes factors influencing changes in bus occupancy rates in Greater Manchester post deregulation and policy impacts.
Studies factors promoting electric bike use among older adults in Taiwan, evaluating attitudes and potential barriers.
Investigates how the design of ghost islands at T-junctions affects road safety and capacity, using predictive modeling.
Explores the elasticities of travel time in transportation demand forecasts within the UK and Denmark.
Develops calibration processes for traffic micro-simulation models to ensure realistic vehicle dynamics and emission estimates.
Assesses the impact of eco-driving on emissions reduction, exploring motivational factors for drivers.
Examines influence of stated preference design on valuation of soft transport factors across different passenger demographics.
Analyzes characteristics affecting car ownership in the UK and Japan, exploring socio-economic differences.
Investigates bidirectional relationships between transport investment, economic growth, and productivity implications.
Evaluates potential capacity increases from implementing moving block signalling in railways amid rising demand.
Compares travel time value across different choice models and their implications for transport policy and investment.
Studies vehicle dynamics in transport micro-simulations to optimize traffic behavior and environmental impact forecasting.
Explores the eco-driving phenomenon, focusing on its economic and environmental benefits, and barriers to adoption.
Analyzes how advanced ticket purchase impacts yield management and revenue maximization in the rail market.
Investigates the potential implications of increasing speed limits on UK motorways, addressing safety and efficiency.
Explores the benefits and challenges of electric bike use in hilly terrains, comparing UK and China experiences.
Investigates the effects of painted cycle lanes on usage and safety, assessing road user behavior in Leeds.
Examines the environmental, social, and economic implications of biofuels supply chains in Taiwan.
Evaluates the correlation between accessibility and customer satisfaction for transport plans in West Yorkshire.
Analyzes the impact of gamification on encouraging greener transport choices among commuters.
Develops a stochastic optimization method for traffic signal networks, improving performance under variable conditions.
Investigates capacity gains from moving block signalling in rail systems under increasing traffic demands.
Analyzes the variability of travel time values across different choice models for transport demand.
Focuses on calibrating microsimulation vehicle dynamics to align simulation outputs with real-world data.
Explores challenges to adopting electric bikes among seniors, aiming to enhance mobility and accessibility.
Examines how parking management policies influence travel behaviors and modal choice dynamics.
Investigates transport infrastructure investments in Uganda and their economic impacts on growth and employment.
Evaluates the efficacy of ghost islands at junctions in local road networks for improving vehicle flow and safety.
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- 1. Development of aTransferable Car Travel Demand Model: A Case Study of Nairobi, Kenya and Dar-es-Salaam, Tanzania Andrew Bwambale | Dr. Charisma Choudhury (Supervisor) | Dr. Nobuhiro Sanko (2nd Reader) 1. Motivation 2. Objectives To investigate the hypothesis that households make joint car ownership and trip generation decisions; To evaluate the local performance of alternative modelling frameworks; To investigate the effectiveness of directly transferred models; and To evaluate the impact of updating procedures on model transferability. 3. Case Study Areas (1) 5. Modelling Framework 6. Methodology Focus will be on spatial transferability of household car ownership and Trip Generation models using data from JICA household surveys - Nairobi (2006) and Dar-es-Salaam (2008). Four alternative structures to be tested in pursuit of the most appropriate modelling framework. M1N/ M1D: A car trip generation model without the car ownership variable to test whether the need for car ownership data can be avoided M2N/ M2D: A car trip generation model with the car ownership variable to test the significance of car ownership on trip generation M3N/M3D A car ownership sub model pre- estimating car ownership for input into the car trip generation model to test performance in circumstances of unavailable/ unreliable car ownership data M4N/ M4D: A Joint car ownership and trip generation model addressing suspected endogeneity between them 4. Case Study Areas (2) NAIROBI DAR-ES-SALAAM 1.1% 3.9% 9.0% 24.7% 31.2% 42.6% 40.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 31.8 74.2 148.5 318.1 530.2 742.3 848.3 Average Household Income (USD) Car Holding Rate by HH Income 1.0% 1.0% 2.7% 5.2% 14.3% 24.0% 45.3% 65.3% 90.6% 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 34.0 102.0 170.1 238.1 340.1 476.2 612.2 1,020.4 1,360.5 Average Household Income (USD) Car Holding Rate by HH Income Budget constraints have resulted in model estimation data shortage in developing countries. A compromise solution could be provided by transferable models. Focus to be placed on transferability of car ownership and car trip generation models (Since private cars are the main source of congestion). However, unreliable car ownership information might limit transferability of traditional trip generation models containing a car ownership variable. Previous studies have not tackled the possibility of using cross-sectional data to develop a joint car ownership / trip generation model based on exogenous variables to avoid this problem
- 2. Perceptions of transportnetwork flood-risk vulnerability Are we prepared enough? Aims • Identify vulnerabilities of the transport network within selected ‘flood-risk’ areas • Establish perceptions of transport network vulnerabilities in case study areas • Ascertain opportunities for improvements to existing strategies in the event of a flooding emergency Expected Findings • Identification of specific training needs in flood-risk areas, directly related to the identification of vulnerabilities in case study areas • Improvement of emergency strategies for vulnerable groups focusing on accessibility in flood-risk areas • Development of a framework for vulnerable area identification and accessibility improvements for policy perceptions of vulnerability Methodology • Undertake a comprehensive analysis of existing literature and preparation techniques for emergency response and vulnerability identification • Audit emergency response information - including training programs and response strategies to identify perceptions of vulnerability in case study areas • Analyse accessibility issues in relation to network limitations for vulnerable groups (e.g. disabled, homeless, isolated elderly people and children) and methods to improve existing strategies “more people will be at risk of coastal flooding each year” UK CEN (2014) Amy Friel MSc. Transport Planning FT Supervisor: Frances Hodgson Source: Environment Agency (2014) Background Over recent years, the rainfall and disaster situations in the UK as a result of flooding have been steadily increasing. As this risk increases, it is necessary to ensure sufficient transport sector emergency preparation for vulnerable areas and vulnerable groups. In the UK: • 5, 000, 000 people in direct risk of flooding • 1 in 6 ‘flood-risk’ properties • ~86% probability of identified areas flooding again in the next 70 years • >300,000 homes without power (Winter 2014) • Sea-levels rose to just 40cm below the Hull Tidal Surge Barrier limit (December 2013) 0 5 10 15 20 25 0 0.5 1 1.5 NumberofPropertiesLost (approx) Thousands Sea-Level Increase (metres AOD) Sea-Levels in the Humber Estuary: Potential Property Loss Key References Golpalakrishnan, C. 2013. Water and disasters: a review and analysis. International Jourlan of Water Resources Development. 29(2), pp. 250-271 Berdica, K. 2002. An introduction to road vulnerability: what has been done, is done and should be done. Transport Policy. 9. pp. 117-127 Environment Agency. 2010. River Hull Flood Risk Management Strategy Report. Halcrow Group Limited.
- 3. Understanding Choice of Departure Airport and its Rela7on to Surface Access: A Case Study of Manchester and Leeds Bradford Airports • To discover the generally accepted distance at which a passenger is not prepared to travel beyond to access a direct flight and how this varies with different journey and passenger types • To assess the role of surface access in passengers willingness to travel to a regional airport of further distance from their home airport, to access a direct flight • Upon the results of the two above objec=ves, would there be a case for the two airports to work collabora=vely. Research Ques=ons: Background Builds upon the work of Johnson, Hess and MaGhews (2014). Their study assessed whether a passenger would be more inclined to take a direct flight from an alterna=ve airport rather than an in-‐direct flight from their home airport. Concluded that irrespec=ve of improved surface access, there was a strong aversion to in-‐direct flights and the passenger would s=ll choose the alterna=ve regional airport. They would s=ll choose the alterna=ve airport if the airfare were to be increased and the access =me longer than their home airport. This study will aGempt to quan=fy the point at which passengers no longer find the promise of a direct flight enough to warrant increased access =me and cost. It will then seek to assess how improved surface access to the airports region wide, would affect passengers propensity to travel to an alterna=ve airport to access a direct flight. Would try to assess the case for strategic coopera=on of the two airports. Scope of the Study Why Manchester and Leeds? In 2010, 20.2% of Manchester Airports patronage originated from or des=na=ons were in the Yorkshire and Humber area, rising from 19.2% in 2009. Yorkshire and the Humber has not only Leeds Bradford Airport, but Doncaster and Humberside too. This would suggest that the people of Yorkshire and Humber are prepared to travel a significant distance to access the wider range of direct flights that Manchester Airport has to offer. First Trans-‐Pennine express provide services to Manchester Airport from across Yorkshire to the airport. Significant? Key References Civil Avia=on Authority. 2010. Passenger Survey Report 2010. London, Civil Avia=on Authority. Civil Avia=on Authority. 2009. Passenger Survey Report 2009. London, Civil Avia=on Authority. Johnson, D. Hess, S. MaGhews, B. 2014. Understanding Air Travellers’ Trade-‐offs Between Connec=ng Flights and Surface Access. Anna Goldie, MSc Transport Planning FT Supervisor: Bryan MaGhews Methodology Will follow stated preference survey techniques and mul=nomial logit models to assess passengers paGern of trades offs Iden=fica=on of a range of important aGributes in the decision making process such as: Air Service type – Full or Low cost Flight Type – Direct or Indirect Access Time Access Mode/s Reliability of Access Modes Price of Access Modes Approach Airports and Access providers such as Trans-‐Pennine Express (MAN) and Arrow Cars (LBA) Secure access to passengers to survey – failing this explore online surveying techniques What Next?
- 4. CONTROLLING REFLECTIVE CRACKINGIN ASPHALT OVERLAYS A Pavement Deterioration Study By: Ahmad Huneidi Supervisor: Mr. David Rockliff 1. Background • Asphalt is a mixture of cement, water and aggregate. It can be used as a surface binder course on the top layer of the pavement. • Pavement layers from bottom to the top layer are sub-grade, capping layer (optional), sub-base, main base, binder course and surface course. • Asphalt cracking is a form of pavement deterioration which is mainly caused by water entering the pavement infrastructure. Other reasons can be due to weathering conditions, traffic loadings and lack of maintenance. 2. Objectives • To control reflective cracking in asphalt and to choose the best cost/effective treatments available. • Treatments to be chosen depending on the deterioration stage. Crack sealing, surface dressing, patching, inlaying and crack injection are suitable solutions. • To identify the causes of the cracking, i.e. water, thermal or traffic related. • Estimating the best time to intervene to maintain the pavement is based on engineering judgment. 3. Comparisons and Limitations • A huge part of the dissertation will focus on comparing asphalt cracking and pavement deterioration between the UK and Kuwait. Specifically to compare deterioration patterns caused by weathering conditions, as in Kuwait the temperature can go up to 50 oC during the day, and may drop 10 degrees and more during nightfall, where in the UK many types of weather can be experienced in a single day. Also, maintenance procedures in terms of asset management comparisons between Kuwait and the UK, where in Kuwait funding and budget is highly available and in the UK highway maintenance budget was cut in the past few years. • The dissertation will also argue the limitations set on highway maintenance, such as budget, as it’s recommended to intervene to fix the pavement, however sometimes it’s better not to intervene to save money. 5. Research Outcomes • Preventing asphalt cracking and on-time intervention. • Introduce a cost/effective pavement maintenance procedures • Heavily deteriorated pavements may lead to car damage and pedestrian/cycling accidents, e.g. potholes. • Identify the conditions of the highway infrastructure in Kuwait and compare it to the UK. 4. Research Methodology • The research will start off defining the asphalt mixture and its’ mechanism. • Functions of a pavement, layers and most common binding courses used. • Detailed definition of the main question. • Designing appropriate thickness with good quality materials to increase pavements’ life-expectancy. • Identify asphalt cracking reasons in Kuwait and in the UK with comparisons. • Identify pavement maintenance procedures in Kuwait and in the UK with comparisons. • Look into highway maintenance and infrastructure budget available and in terms of asset management. 6. References • J.M. Rigo, R. Degeimbre, L. Francken (2010) Reflective Cracking in Pavements: State of the Art and Design Recommendations. Oxford, UK. • L. Francken, E. Beuving, A. Molenaar (2004) Reflective Cracking in Pavements: Design and Performance of Overlay Systems. London, UK. • T. Harvey (1995) Structural Design of Asphalt Concrete Pavements to Prevent Fatigue Cracking. California, USA. • J. Baek (2010) Modelling Reflective Cracking Development in HMA Overlays. Illinois, USA. • Button & Lytton (2006) Guidelines – Synthetics in HMA Overlays. • Online references: Tensar International, Maccaferri, Adept & Institute of Asphalt Technology. • References from Kuwait: Arab Planning Institute, Department of Transport, and Ministry of Public Works.
- 5. Source :http://www.transportumum.com/jakarta andhttps://www.google.co.uk/maps/place/Jakarta Commuter Line TransJakarta(BRT) *Household Travel Survey (HTS), Commuter Travel Survey (CTS) Source : Nobel, et.al 2013 Jakarta Tangerang city Bekasi Depok City and Bogor City (2002) 262 (2010) 423 ↑1.6 times (2002) 247 (2010) 344 ↑1.4 times (2002) 234 (2010) 338 ↑1.4 times (unit) in 1.000 Graph modified by researcher based on Preliminary figures of JUTPI Commuter Survey In Total (2002) 743 (2010) 1105 ↑1.5 times Congestion Jakarta loss IDR 12,8 Trillion in material aspect such as time per year (finance.detik.com,2013) Stress Relation between congestion and driver stress has found in high congestion (Wickens and Wiesenthal, 2005) Pollution Jakarta’s people loss IDR 35 Trillion per year in health issues caused by pollutions (jurnas.com,2014) Transport Issues in Jakarta Trip from Outside Jakarta Per Day Strength of Car dependency in Jakarta Relationship Between Social Status and Car Use Determine derived issues such as instrumental and emotional issue Recommendation for transport policy in Jakarta Behaviour , Habit and Intention relation Based profile segmentation to see how strong car dependency is. • Gardner, 2009 • Brujin et.al, 2009 Changes of People Behaviour Based on possibilities to attract people to change their behaviour • Stradling et.al, 2000 Intention Attitude (behaviour, intention and habit) Perceive Behaviour Control Subjective Norm Data collection Using online questionnaire (purposive and snowballing sampling) Data Cleansing Validity and Reliability Check Grouping the factors Based on TPB analysis to look at driver motivation Anabel, 2005 Factor analysis Infrastructure Improvement In Public Transport, Traffic Management or supporting facilities Behaviour Approach Using ‘Push’ or ‘Pull’ approach (Stardling et.al 2000) or Smarter Choices system (Cairns et.al,2008) Driver Motivation Statistical Analysis Recommendation for Jakarta’s Transportation Behaviour Methodology Objective Background Jakarta Profile • Area 664,01 Km²* • Population 9,604,329* • Household 2,508,869* • Total road length is 7,650 6.2% of total area of the city • 17.1 million trips/day • (Source : http://www.kemendagri.go.id, BPS, UI and APRU 2010) “Is social status become a reason behind car use in Jakarta? ” Research Question • Steg, 2005, indicate that car use can be a variable to show status symbol in a group. • Hiscock et.al, 2002 identify that prestige is one of factors that influence car use in Scotland. • Shove 1998, Sheller & Urry, 2000 and Dant & Martin, 2001 mention that car gives values added to their owner on social status. Theory Planned Behaviour (TPB) is adapted from Ajzen, 1991 use to finding the sets of behavioural of human being, it will measuring people attitude (behaviour, intention and habit (Gardner, 2009)), normative and control belief. Data collection will using online questionnaire in Indonesian Language. It will distributes to people who is commuting inner and to Jakarta. Grouping the data from questionnaire based on factors analysis refers to Anabel, 2005 research, and do data cleansing by checking validity and reliability with cross tab analysis. Thus, this analysis will use SPSS to look at relationship strength between variables and finding the most affecting factor. ‘Push’ and ‘Pull’ system are psychological approach to change people behaviour by setting the policy adapted from Stradling et.al, 2000. And to strengthen the policy, smarter choice can become other option to reduce cost value. For infrastructure development will address to government budget and regulation as their function to provide better facilities for citizens. Potential Risk • Data validation unfitting with the objective and misunderstanding perception with researcher intent. And its potentially privacy question that might annoys respondent (Frederick, 2008) • Respondent resistant to answer with honest because it interfere their status symbolic (Steg ,2005) • Limitation of this research particularly find the relationship between car users in Jakarta with social status. Jakarta Maps Picture Source : google images for congestion in Jakarta Researcher : Ayu Kharizsa (ml12a28k@leeds.ac.uk), MSc, Transport Planning Supervisor : Dr. Ann Jopson (A.F.Jopson@its.leeds.ac.uk) Second Reader : Frances Hodgson (F.C.Hodgson@its.leeds.ac.uk) Mode shares by Purpose Source : Ajzen, 1991
- 6. Anastasios Leotsarakos –MSc (ENG) Transport Planning and Engineering Supervisor: Dr. Haibo Chen University of Leeds - May 2014 OBJECTIVES The aim of the project is to: Identify the main accident characteristics responsible for the formation of queues. Create a model that quantifies the effect of these characteristics. Predict the potential of a queue to be formatted and its characteristics (maximum length and duration), when an accident occurs. METHODOLOGY CASE STUDY The project investigates the case of Attiki Odos, a motorway in Athens, the capital of Greece, functioning as the Athens Ring Road, providing connection with the Athens International airport, passing through urban and rural areas. The total length of the motorway is 65 km while there are 3 lanes plus an emergency lane in each direction. In the median zone of the motorway runs the suburban railway. DATA Accident and traffic data from Attiki Odos motorway from 2007-2010. Total number of accidents: 3,321. Number of accidents actually used: 1,442. Traffic data from loop detectors in 0.5 km intervals and 5 min frequency. A total of 32 variables. VARIABLES 1 Type of day 17 Speed (km/hr) 2 Accident duration 18 Lane Volume (pcus/hr) 3 Accident type 19 Queue max length (km) 4 Collision type 20 Queue duration (min) 5 Fatalities 21 Rainfall 6 Injuries 22 Alignment 7 Number of Lanes 23 Geometry downstream 8 Left Lane 24 Geometry upstream 9 Middle Lane 25 Tunnel down 10 Right Lane 26 Interchange down 11 Emergency Lane 27 Toll down 12 Lane type 28 More than one down 13 Number of Vehicles 29 Tunnel up 14 PC 30 Interchange up 15 PTW 31 Toll up 16 TRUCK 32 More than one up BACKGROUND 50% of delays in motorways are non-recurrent (incident produced) When an accident occurs the road capacity can be reduced: a shock-wave of slow-downs is created that, propagates downstream and can result in the formation of a ‘platoon’ or queue behind. A very important factor in the development of accident management strategies is to identify and quantify the conditions affecting the nonrecurrent congestion caused by accidents once they have occurred. Identify Shockwaves Identify Queues in Shockwaves (Length and Duration) Create a Model that Calculates Queues: f(Qlength) = ... f(Qduration) = ... Attiki Odos, Airport InterchangeSchematic shockwave caused by traffic accident
- 7. • Hills andactivity related issues recognised as key issue by Gatersleben & Appleton:(2007) in their cycle to work study in a hilly area of Surrey. • Figure 2 displays what they found to be key factors leading to a bad cycling experience. 24% 19% 13% 8% 36% Figure 2: Factors relating to bad cycling experiences Bad Weather/Darkness Activity related issues: hills & feeling tired Traffic issues Mechanical Other Source: Gatersleben & Appleton (2007) What are electric bikes? Electric bicycles (also known as Pedelecs and e-bikes) are bicycles which offer the rider electrical assistance when pedalling. This comes from a battery power source. Expected findings: - Technologically e-bikes are now a viable form of transport. - Lack of awareness of the benefits from the public and policymakers which is limiting the uptake of e-bikes amongst most groups. - The increased Cost of an e-bike is a key barrier to uptake, particularly for lower-income groups and those new to cycling. Industry bodies recommend >£1000 for a quality model. - Concerns which prevent people using conventional bikes will still form a barrier. These include road safety and weather (Rose 2013). Background Methodology & Data collection 1. Qualitative interviews with e-bike retailers, manufacturers and industry experts. The findings will feed into and complement the questionnaire survey. 2. Questionnaire survey of existing e-bike users and those who do not currently cycle. This will assess the impact of the technology on travel habits of existing owners and the attitudes of non- owners . 3. Analyse & Triangulate both qualitative and quantitative data to gain significance and depth of understanding. Key references consulted: Gordon, E., Xing, Y., Wang, Y., Handy, S., & Sperling, D. (2012). Can Electric 2-wheelers Play a Substantial Role in Reducing C02 Emissions?. Institute of Transportation Studies, University of California, Davis. Rose, G. (2012). E-bikes and urban transportation: emerging issues and unresolved questions. Transportation, 39(1), 81-96. Dill, J., & Rose, G. (2012). E-bikes and transportation policy: Insights from early adopters. Transportation Research Record: Journal of the Transportation Research Board, (2314), 1-6 Gatersleben, B., & Appleton, K. M. (2007). Contemplating cycling to work: Attitudes and perceptions in different stages of change. Transportation Research Part A: Policy and Practice, 41(4), 302-312. How much of a barrier are hills and intense physical activity? Where is the potential for e-bikes? It has been acknowledged that e-bikes can encourage cycling amongst: • The Elderly • Physically disadvantaged • Cyclists in hot, hilly or windy areas • Those wishing to avoid the need to change clothes (see Rose 2012 & Gorden et. al. 2012). Source: COLIBI 2013 Electric bike sales Sales have been strongest in China with Germany and the Netherlands jointly making up 65% of the European market in 2012. Research questions arising 1. Can e-bikes encourage more cycling trips in hilly areas and amongst those less able in the UK? 2. What are the barriers to e-bike ownership, given the slow take-up in the UK? Are electric bikes a solution to hills? A UK perspective. A hub-mounted electric motor A frame-mounted electric motor Folding electric bicycle Student: Alexander Lister Course: MSc. Transport Planning Dissertation Supervisor: Frances Hodgson 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 2010 2011 2012 E-bikesales Year E-bike sales 2010-2012 Great Britain Germany Netherlands 45% 20% 5% 5% 4% 21% Europe 2012 e-bike sales share Germany The Netherlands France Italy Great Britain Other(s)
- 8. Monica Corso -ts10mec@leeds.ac.uk MSc (Eng) Transport Planning and Engineering Supervisor: Daniel Johnson May 2012 Bing Li – MSc Transport Planning and Engineering Supervisor: Dr. James Tate E-mail: ml12b2l@ leeds.ac.uk Institute for Transport Studies University of Leeds 05 - 2014 BACKGROUND OBJECTIVES CASE STUDY ➢ The main aim of this project is to better understand the impacts of traffic congestion on fuel consumption and vehicle emissions performance in the study area – Headingley. ➢ Two key objectives: Actual Tracked Vehicles Data in Headlingley from RETEMM Project (Speed, Acceleration, Gradient) One Vehicle with PEMS Model Validation and Data Obtaining – Real and Predict Emissions & Fuel Consumption VEHICLE EMISSIONS FUEL CONSUMPTION ➢ Road transport is the main source of air pollution in urban areas. ➢ Speed profiles will help to run emission model. ➢ Speed profiles are obtained using a GPS data logger and the data is historic collected. ➢ Road gradient is considered which will be used in PHEM through vehicle specific power (VSP) formula and to make model more accurate. ➢ Analysing the relationship between congestion and vehicle emissions, and how driving behaviours in traffic congestion affect emissions ➢ Fuel consumption is greatly influenced by road gradient because it is related to different engine load. ➢ Fuel consumption usually increases under congestion and the changing of driving behaviours makes contributions to the increasing part. ◎ Using actual tracked data to study how driving behaviours and vehicle movements adapt to congestion and the effect on tail-pipe emissions. ◎ Analysing how driving behaviours and vehicle movements influence fuel consumption under congestion. ➢ Health Effect: Public Health England(PHE) said 5.3 per cent of all deaths in over-25s were linked to air pollution, which is more than road accidents. ➢ Emission Trends: The emissions of petro vehicles (e.g. NOX) decreases from Euro0 to Euro5, however, it is almost unchanged for diesel vehicles and increases from Euro3. *Real-world Traffic Emissions Monitoring and Modelling (RETEMM). EPSRC January 2008, Grant Reference: GR/S31136/01 Five vehicles with GPS data Analysis and Comparison Emission Model – PHEM Real Observations (CO2) 0 200 400 600 800 1000 0.0000.0010.0020.0030.0040.0050.006 Time (seconds) NOx(g/s) 0 200 400 600 800 1000 01020304050 Time (seconds) VehicleSpeed(kmh 1 ) Speed Profile NOX Profile
- 9. •Review of relevant literature •formulation of questions for interviews Methodology Step one •Interview of policy makers and Non Governmental Organizations (NGOs) • Interview analysis. Methodology Step two •An appraisal of the factors that have influenced the focus on CO2 reduction in transport in the UK; •Exposition on the consequences of such focus. Expected Findings Benedictus Dotu Nyan ID: 200819480 Sustainability (Transport) Supervisor Antonio Ferreira (Dr) Caroline Mullen (Dr) Second Reader TRAN5911M Background- emergence of focus on CO2 reduction in the UK • Stern Review (2006) urged transition to a low carbon economy. • UK Climate Change Act (2008) :- transport is a major source of CO2 and other Greenhouse gases (DfT, 2012). • Carbon Plan (2011) :- move toward achieving an 80% reduction in and CO2 other Greenhouse gases (DfT, 2012) • (DECC, 2014) CO2 emissions from the transport sector in the UK in 2013 accounted for ¼ of all domestic CO2 emissions Objectives Identify factors that have influenced the focus on CO2 reduction in the UK. Examine the pros and cons of focusing on CO2 reduction in the UK. Research Questions What are the factors that have influenced the focus on CO2 reduction in the UK? What are the pros and cons of focusing on CO2 reduction in the UK? Problem • Humanity has already transgressed the climate change planetary boundary . • It is based on two critical thresholds- CO2 and radiative forcing. • Exceedence of 350 PPM of CO2 and 1 watt of radiative forcing will result to irreversible climate change.; However, • The biodiversity boundary has been transgressed; • Change in land use has become problematic (Rockstrom et al., 2009). • Ambient air pollution (PM2.5, PM10 NO2, SO2, etc.) was responsible for 3.5 million deaths in 2012 (WHO, 2012); Google Image
- 10. DEVELOPING TRIP GENERATIONMODELS: COMBINING SURVEY AND MOBILE PHONE DATA Author: Christopher O. Edeimu Supervisor: Dr. Charisma F. Choudhury A trip is a one way journey and may be classified as home or non-home based. Trip rates are the rates at which trips are generated. Can be considered the rate socio-economic activities are loaded onto the transport network. They indicate the network capacity to plan and provide for. They are influenced by land use and socio-economic attributes of the population. ABSTRACT Their accuracy and reliability depend on the reliably of the data. The cost of data gathering and trip rates estimation make this a major challenge in most developing countries. However, mobile phone data are accurate and reliably generated and, properly harnessed, presents a low cost, alternative source of transport planning data. Trip generation has been studied at the: • Aggregate level employing linear regression and categorical analysis. (Vickerman and Barmby, 1985). • Disaggregate level using discrete choice models. Regression method will be adopted in this study (Washington 2000), because they: • Facilitate identification of variables that are correlated with trip origination. • Are useful for prediction and policy impact assessment Neumann et al, (1983) directly estimated all-purpose trip production rates using traffic ground count to data. Obtained estimates within 96% of true rates. Caceres et al., (2007) inferred OD matrices from mobile phone data. OBJECTIVES Contribute towards developing trip generation models that countries with limited resources to undertake household surveys can use to reliably estimate trip rates. Specifically: • Develop regression-based trip generation models combining mobile phone CDR and socio-economic variables. • Improve reliability of trip rate estimation. • Reduce data requirements for trip rates estimation. • Reduce trip rates modelling cost. To examine the feasibility of combining mobile phone CDR and socio-economic data in trip rates estimation. Two questions to be investigated. 1. Will trip rates derived using mobile phone data be statistically different from those derived using household survey data? 2. If yes, what might the reason(s) be? EXPECTED OUTCOMESLITERATURE REVIEW REFFERENCES 1. Arabani M. and Amani B. 2007. Evaluating the Parameters Affecting Urban Trip-Generation. Iranian Journal of Science & Technology, Vol. 31, No. B5, pp. 547-560. 2. Caceres et al. 2007. Deriving Origin–Destination Data from a Mobile Phone Network. IET Intelligent Transport System Vol. 1, pp. 15–26 3. Neumann E. S. et al. 1983. Estimating Trip Rates from Traffic Counts. Journal of Transportation Engineering, Vol. 109, No. 4, pp. 565-578. 4. Ortúzar, J. D. & Willumsen, L. G. 2001, Modelling Transport, Third Edition, Wiley, New York. 5. Vickerman, R. W., and Barmby T. A. 1985. Household Trip Generation Choice: Alternative Empirical Approaches, Transportation Research B, vol. 19, no. 6, pp. 471-479. 6. Washington, S. 2000, "Iteratively Specified Tree-based Regression: Theory and Trip Generation Example", Journal of Transportation Engineering-Asce, vol. 126, no. 6, pp. 482-491. Traffic Assignment Modal Split Trip Distribution Trip Generation Trip Rates Because they feed into every aspect of the transport planning process they should be properly and reliably estimated. Inaccuracies will be greatly magnified in inefficient and ineffective transport policies and system. Area-wide, all-purpose linear regression estimates of trip generation rate for motorized journeys suitable for systems with limited resources and access to appropriate data. • Area-wide, all-purpose, because they produce equally accurate results. (Coomer and Corradino, 1973). • The implicit assumption by conventional models of mutually independent trips may not properly reflect behavioural reality. (Goulias et al. 1991). Statistical Methodology for model estimation Trip Generation Model 𝑡 𝑘 = 𝛼 + � 𝛽𝑖 𝑋𝑖𝑖 + 𝜀 𝑛 𝑖=1 (𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑒𝑒. 𝑎𝑎, 1983) 1. Divide study area into units (TAZ). 2. Map mobile data to TAZ. 3. Infer residual traffic counts. 4. Regress residual traffic counts on socio- economic variables. 5. Results: all-purpose TAZ trip generation rate. • 𝑡 𝑘 = Area−wide all−purpose trip rate for TAZ k (dependent variable) • 𝑋𝑖𝑖 = Matrix of socio−economic variables (explanatory variables) • 𝛽𝑖= Vector of coefficients Error Tests Model Estimation Model Validation Model Calibration Model Specification Tested Trip rates to be relied on for their purposes anywhere in the transport planning phase. Step Equation R2 Parameters 1 𝒗 𝒌 = 𝜶 + 𝜷 𝟏 𝑿 𝟏 + 𝜺 d% 𝑿 𝟏 2 𝒗 𝒌 = 𝜶 + 𝜷 𝟏 𝑿 𝟏 + 𝜷 𝟐 𝑿 𝟐 + 𝜺 e% 𝑿 𝟐 . ... … … … n 𝒗 𝒌 = 𝜶 + 𝜷 𝟏 𝑿 𝟏 + 𝜷 𝟐 𝑿 𝟐+. . +𝜷 𝒏 𝑿 𝒏 + 𝜺 f% 𝑿 𝒏 • All parameter to have the required sign and order of magnitude. • R2 will be significant in determining validity of the results. Statistically large samples sizes are critical to proving the significance of relationships. • The expected sample of CDR data is over 900m.
- 11. • The MohringEffect is the background for the reimbursement guidance of Concessionary Travel from the UK Department for Transport. • In England, the Concessionary Travel has been introduced in 2006 for elderly people and disabled residents allowing free travel in off peak time. In 2009/2010 concessionary passengers on local bus represented the 30% of local bus trips. •The principle for reimbursement to bus operators was “NBNWO” , “No better no worse off” than without the scheme: costs incurred by carrying extra passengers may be significantly different depending on behaviour of the operator facing the extra –demand. Operators may: • Allow for higher load factor without any additional service • Run larger vehicles • Run additional service to the route, leading to the Mohring Effect. The power relationship between frequency and demand is challenged by real world considerations, such as: • Indivisibilities, desire to maintain “round numbers” for service level • Load factor constraints, passenger constraints • Predatory competition Does the Mohring effect really exist? Student: Dario Nistri Supervisor: Jeremy Toner - Second Supervisor: Antony Whiteing Background What is the Mohring effect? Demand Q= 1 Opt. Number of bus =B* • The Mohring effect is a form of economy of scale by user side in public transport services. For scheduled and urban public transport, an increase in frequency, produces economy of scale for users in term of time savings. •Supposing a Welfare Maximising operator, Mohring (1972) states that whether it occurs an exogenous increase of travel demand, the bus service would increase with the square-root. Mohring Square root Opt. Number of bus =1.40B* • Supplying the service with additional 40% bus units, travellers double Meaning of the rule Task 1 Task 2 Demand and cost estimationMax Load Factor Crowding Threshold Overload Departures Does elasticity is =0.5? How much is different? Square Root Method Mohring Effect Size of Mohring Factor Size of Mohring Factor Policy implications Methodology Objectives Expected results References However a second order demand of commercial travellers is generated beyond concessionary extra traffic. •Investigation of operators behaviour when the increase of the demand occurs, attempting to identify the crowding threshold above that the operators upgrade the service increasing the frequency. •Estimation of the effect of the agreement for full or partial reimbursement of Concessionary Travel on service level . •Estimation of the size of Mohring Factor in the context of unregulated market populated by profit-maximising operators. •At network level, calculation of costs and Load Factor change due to the introduction of Concessionary Fare, in case of full and partial reimbursement . Application of Abrantes and Last methodology to calculate Mohring Effect. •Application of the Square Root at route level to calculate Mohring Factor, taking in account two routes one with the demand double of the other. Hp 2 Hp 1 Abrantes & Last Method Concess. pax Generated Pax Task 1 Task 2 •Abrantes and Last study established two crowding threshold that we consider such as milestones. So it is reasonable to expect that the threshold for the data taken in account would be between 85% and 100% of load factor. •The condition “no better no worse off” , if not fully applied, may prevent the profit maximising operator to increase the service, allowing the load factor to increase. •The size of Mohring Factor in the context of unregulated market populated by profit-maximising operators is expected to vary that estimated for the welfare maximising scenario. •Nelltorph et al. (2010) proposed the value of 0.6 in welfare- maximising framework. Abrantes and Last (2011), studying the commercial decisions by operators in three English Metropolitan areas, calculated the values reported in the following table. •Abrantes, P., & Last, A. (2011). Estimating additional capacity requirements due to free bus travel. •Toner J.P.(2013). Mohring Effect: theory and Existing Evidence. Institute for Transport Studies. B*= optimal n. of bus /hour C= unit cost to produce bus/h Q= passenger / hours V= value of time • If an hexogen change doubles the travel demand, it can be supplied with 40% additional bus units Demand Q= 2
- 12. Introduction Landlocked DevelopingCountries (LLDCs) face peculiar transport problems, particularly in freight transport, because of their dependency on other countries co–operation for access to international trade routes. Freight costs per km in most LLDCs are more than 50 percent (value of export), higher than in United States of America and Europe. Transport costs can be as high as 75 percent of the value of exports (Faye et al., 2004). A number of studies have been conducted on freight transport problems encountered along the northern corridor; however, very few have used system dynamics thinking to analyse and present these problems. Problems faced Dependence on transit neighbour: neighbours’ infrastructure; administrative practices; peace and stability. Long distance from the sea. High freight transportation cost. Aims and Objectives Identify problems faced by transporters. Identify factors hindering efficiency of cargo clearance along the Northern Corridor. Carry out system analysis of factors that hinder freight transportation along the Northern Corridor and develop causal loop diagrams that describe feedback mechanisms between these factors. SYSTEM ANALYSIS OF BARRIERS TOWARDS FREIGHT TRANSPORTATION IN LANDLOCKED DEVELOPING COUNTRIES: A CASE OF ROAD FREIGHT TRANSPORTATION IN UGAANDA Student: OKELLO Cypriano: (Msc.) Transport Planning Supervisor: Dr. Astrid Guehnemann; Co-supervisor : Prof. Paul Timms University of Leeds Methodology Causal Loop Diagrams (CLDs) The CLDs are important tool for representing the feedback structure of systems (Sterman, 2001). The CLDs are excellent for: Capturing hypothesis about causes of dynamics Eliciting and capturing mental models of individuals/teams Communicating important feedbacks believed to be responsible for problems Data collection Face to face interviews using semi–structured questionnaires Sampling techniques Purposive sampling (Ministries, Departments and Agencies) Missing voices to be included (allows for flexibility) Data analysis Draws out patterns from concepts and insights Data presentation Causal Loop Diagrams will be used to describe feedback mechanisms between freight transport problems identified. Scope Main focus on road transport along the northern corridor, from Mombasa to Kampala Malaba Border Post (Malaba–Uganda and Malaba–Kenya) 50% of Travel Time: waiting at BPs & other stops References FAYE, M. L., MCARTHUR, J. W., SACHS, J. D. & SNOW, T. 2004. The challenges facing landlocked developing countries. Journal of Human Development, 5, 31-68. STERMAN, J. D. 2001. System Dynamics Modelling: TOOLS FOR LEARNING IN A COMPLEX WORLD. California management review, 43.
- 13. Car Dependency inthe City of Leeds: The Impact of Infrastructure and Culture Objectives The purpose of this dissertation is to explore some of the key questions in relation to car dependency within the City of Leeds: • What is the extent of car dependency in the city? • What are the main causes for it? o In particular what is the extent of the role of two of the main contributing factors towards car dependency: Attitudes and Infrastructure On gaining a measure of these issues, this dissertation will set out what could be done to reduce it through Policy Changes and/or Capital Investments. Background Campaign for Better Transport 2012 Annual survey that ranks each city by its dependency on cars. Cities are scored on: Of the 26 cities included in the scorecard, Leeds was 20th overall In relation to amount of car use it was joint 24th Why is car use bad? • Roads are Congested • Economic Impacts e.g. disutility of time spent in traffic • Accessibility Impacts e.g. people unable to get to where they want to • Environmental Impacts e.g. pollution, effect on health, carbon • Social e.g. effect of inactivity, leading to obesity issues Why is it particularly bad for Leeds? • Car ownership in Leeds is still growing o 2001-2011 – 2% increase in number of households that have a car (ONS, 2001 and 2011 Census) • Two-way AM peak traffic volumes increased by 10% between 1990-2012 • Population still rapidly growing: o 11.8% larger in 2021 from 2011 with 840,000 people living within the Leeds district area (ONS, Sub-National Population Projections, 2012) and; o 74,000 new homes planned to be built between 2012-2028 (LCC, Core Strategy, 2013) • A net importer for jobs, with more travelling into the city to work than travel out: o Circa 460,000 people employed in Leeds (ONS, Nomis Job Density Data, 2011) o 50,000 more than flow out of Leeds (ONS, Commuter Annual Population Survey, 2011) Methodology 1. Desktop Study • Examine the existing infrastructure in Leeds - including GIS analysis • Determine if there is any validity in claims that Leeds is a car dependent city due to infrastructure compared with cities that scored well on the CfBT scorecard 2. Opinion Survey • Scope - Aimed at car users commuting into Leeds, focusing on attitudes to car use, infrastructure, public transport and active travel provision from an individual perspective • Influence – Survey will be informed by existing literature e.g. Linda Steg’s article, Car Use: Lust and Must (2005) in which surveys were used to examine motives for car use • Concepts from the TPB model will also be used to inform the direction of the survey • Method - Survey to be carried out using an online survey website, circulated through Metro’s business contacts who are signed up with the Travel to Work team • Sample Size – Circa 200. If this cannot be attained through the on line survey, manual surveys will be carried out at key locations in city centre e.g. car parks • Analysis - Designed to allow for ANOVA to explore the variations in people’s responses in respect of their attitudes towards different aspects of transport and car use • T-tests to be used to demonstrate whether there is any significance in the different responses from the different groups • Analysis will enable results to be tied back to the aims and objectives to provide suggestions for possible targeted policy changes or investments to reduce car dependency Key Thoughts – Attitudes Steg (2005) – Car Use: Lust and Must • Car use not just about fulfilling a function i.e. getting people to work. It has a large symbolic status, with pleasure being derived from its use, even just for commuting “the car is much more than a means of transport” • People use cars because of the experience of driving, because of its status • This reinforces people’s choice to drive • Policy needs to target these attitudes - offer a real alternative in public transport? Ajzen (1985) – Theory of Planned Behaviour • People’s choice of mode such as car, is dependent on their attitudes, social norms and perceived behavioural control • In order to change people’s behaviour and choice of car as a mode, you need to target these areas o Offer real alternatives to the car, change the social norm so that public transport / active travel is how you get about in Leeds o Improve the image of public transport / active travel and discourage that of the car Ellaway et al (2003) – In the Driving Seat • Explored the psychological benefits associated with private and public transport to help explain why so many people drive i.e. car has greater psychological benefits than public transport. • Suggests that in order to encourage reduction in private car use policy must take these types of benefits people derive from car use into account Key Thoughts – Infrastructure Leeds’ transport system focuses around its city centre, with a large number of commuting trips coming in from outside the Outer Ring Road (ORR) • 29% of commuting trips to Leeds city centre made within the ORR during AM Peak • 71% from further afield – people commuting in are more likely to use a car • 45% of total trips made by car • 30% by rail and 25% by bus (LCC, Transport for Leeds Project 2008/09) Car • Well established road and motorway network built in ‘spoke and wheel’ layout • Makes travelling by car easy and allows direct access to city centre from suburban areas and other districts • Large amount of car parking in city centre – circa 22,000 spaces (LCC, Annual Parking Report, 2011/12) Rail • Network serves limited radial corridors, with few stations within ORR • High rail demand, circa 16,800 arriving in Leeds City Station during morning peak in 2013, compared to 12,400 in 2004 (LCC, Cordon Count Data) • Figure has tapered off in recent years, suggesting network is reaching capacity • There are plans to expand network capacity – new stations, longer trains and improvements to the lines to cope with more train services Bus • Well established network – Patronage remains at consistent level year on year • Bus mode share for commuting trips higher than car within ORR – 59% • Outside ORR it is 18% and 47% for car (LCC, Transport for Leeds Project 2008/09) • Long dwell times at stops due to boarding – smartcard ticketing is being phased in • Bus punctuality – 88.6% run ‘on time’ (1minute early and 5 minutes late) (Metro, MetroFacts, 2009/10) • This falls short of Traffic Commissioner’s target of 95% • Large journey time variability Rapid Transit • City has none – largest city in Europe to have nothing • Trolleybus networked planned to provide a real alternative to car. • However, only one initial line so limit impact Cycling/Walking • Little infrastructure for cycling, although it is improving e.g. Cycle Superhighway • However, city geography makes it difficult to encourage large numbers of cyclists • Long distances between suburban areas and city centre 2 Way Traffic Cordon Flows For All Vehicles in AM Peak (LCC Monitoring) 1990 2004 2012 1990-‐2004 Growth 2004-‐2012 Growth 1990-‐2012 Growth 145,474 163,098 160,484 12% -‐2% 10% Chris Payne Supervisor: Ann Jopson Accessibility and planning Buses and trains quality and uptake Cycling and walking as alternatives Driving and car use Larger squares = be9er rankings in category Source: Steer Davies Gleave, 2009 Source: Leeds City Council Leeds Transport Geography Congested Routes in Leeds District
- 14. • Complex roadnetwork with 245,000 miles worth of road (DfT, 2012) • 35 million vehicles on British roads in 2013 and that is a 1.5% increase from 2012 (DfT, 2014) • Roads don’t last forever, wear and tear, accidents means that there is a need for increased investment to being maintained • The maintenance of local authority managed roads is being reduced: - 2009/10 £3.3 million - 2010/11 £3.1 million - 2011/12 £3.0 million (DfT, 2013) • By 2020/21 £6 billion will have being invested to help repair and sustain local roads (Great Britain & HM Treasury, 2013) • Must use resources more efficiently, how is this decided? How should it be decided • Providing a service for the public, so ask the public what they think • The customer satisfaction surveys involve 46 local authorities • Cross comparison of two models, will be the same except with the addition of customer satisfaction in one of them • What determines the cost? Cost= f(Type of treatment, time constraints, Customer Satisfaction, availability of resources, traffic management, utilities) • Estimate the significance, size and signs of the variables based on the economic background • Problems, which could be encountered: missing variables, errors in variables larger data set needed, the independence of the authorities • To solve these problems appropriate tests will be taken Disadvantages • Only when habits are changed can there be a true value • Instruments for measuring customer satisfaction not readily available • Difficult to apply costs to a 5 point scale (Abou-Zeid 2008) • Personality and taste will affect the results making it biased • Not consistent when surveys are repeated because there will be a different range of income, age, gender, employment 𝐻0 : Customer satisfaction plays a valid role 𝐻𝐴 : Customer satisfaction plays no significance Abou-Zeid, M. Moshe B, and Michel B. 2008. Happiness and travel behavior modification. Proc. of the European Transport Conference. Department for Transport. (2012). Road lengths in Great Britain: 2011. Department for Transport. (2013). Road Conditions in England: 2012 Department for Transport. (2014). Vehicle Licensing Statistics: 2013. Great Britain & HM Treasury. (2013). Investing in Britain’s future. Vol 8669. Stationary Office. Highways Agency. (2014). Listening to our customers. Olsson, L. Friman, M. Pareigis, J. Evardsson, B. (2012). Measuring service experience: Applying the satisfaction with travel scale in public transport. Journal of Retailing and Customers Satisfaftion. 19, pp. 413-418. Charlotte Stead- 200386644 MA Transport Economics Phill Wheat • 𝐻0 : Customer satisfaction plays a valid role - Fail to reject the null hypothesis, - Customer satisfaction should be used - How can it be improved? • 𝐻𝐴 : Customer satisfaction plays no significance - Can reject the null hypothesis - What are the alternatives • The problems encountered in the model • How this model can be improved Advantages • Used to assess the non monetary costs such as time, smoothness of the journey, cleanliness (Olsson 2012) • Surveys are used to assess how well services are meeting expectations • This is needed to influence investment decisions “Understanding the needs of our customers is an integral part of the Agency’s operations. To help us achieve our vision we need help.” (Highways Agency, 2014) All roads needs maintenance Highway Maintenance Strategy
- 15. SOURCE: Public TransportAuthority of Western Australia Workplace test group: One40 William PHOTO CREDIT: Hassell One40 William Results Oral presentation Written dissertation Summary report to participating organisations Analysis Data cleansing Cross- tabulation Principal Components Analysis Multiple Discriminant Analysis Data Collection Questionnaire: Test group - One40 William Control group - same/similar organisations, alternate sites Literature Review Car dependency Land use & urban design Mode shift Habit disruption TIPPING THE SCALES Do active and public transport facilities at the workplace reduce commuter car use? Researcher: Catherine Wallace (ts13clw@leeds.ac.uk), MSc Sustainability (Transport) Supervisor: Ann Jopson (A.F.Jopson@its.leeds.ac.uk); Second Reader: Frances Hodgson Institute for Transport Studies FACULTY OF ENVIRONMENT Increased use of active and public transport for commuting? Office building integrated with train station Close to bus stops & central bus station Free Transit Zone End of trip facilities Parking restrictions and high fees Context Objectives Methods Analysis Research QuestionsIntroduction CAR DEPENDENCY § Private car use is reaching unsustainable levels in many industrialised countries (Kenworthy & Laube 1996). § There is a particular interest in reducing the negative effects of congested commuter traffic in cities (O’Fallon et al. 2004). § Many of the negative effects (to the economy, health and the environment) seemingly cannot be mitigated by technological improvements alone (Bamberg 2007). Behavioural change is required. § The psychological motives for car use are not just instrumental (practical) ones, like travel time and convenience. Car use has an affective/symbolic function – it represents power and control; it is a status symbol and extension of self (Steg 2005). LAND USE & URBAN DESIGN § ...have a cumulative effect on travel behaviour (Litman, 2014) § Parking management can reduce car trips between 10-30%; multi-modal site design also thought to contribute (Litman 2014) § When examining commuter mode choices, most studies look at the impact of residential location and access to transport services and infrastructure from home (Vale 2013) § Proximity to public transport and quality of active transport facilities near home affect mode choice (Naess 2009) § People may also self-select their home location to reflect their preferred mode choice (Cao et al. 2009), but self-selection may play a lesser role in workplace location and particularly workplace relocation (Vale 2013) § Research gap: how do workplace (destination) facilities/ access influence commuting choices (Vale 2013; Litman 2014) MODE SHIFT § Commuting accounts for 15-20% of trips, but >50% of congestion (Litman, 2014) § To change behaviour, you change the person or the conditions (Stradling et al. 2000) § City centres, where many workplaces are focused, ”are more amenable to alternative modes” (Litman 2014, p.18) à Is changing the conditions at a city centre workplace enough to change commuter behaviour? HABIT DISRUPTION § Commuting is habituated (de Brujin et al. 2009) § To break a habit, you need an impetus that makes people re- evaluate their choices (Handy et al. 2005; Bamberg 2006) § Research gap: what disrupts commuter habit? (de Brujin et al. 2009) Develop and pilot online questionnaire: § Travel behaviour before and after office relocation § Commuting habits (Self-Reported Habit Index, adapted from de Brujin et al. 2009) § Psychological motives (affective & instrumental factors, adapted from Steg 2005 and Bergstat et al. 2011) § Personal characteristics (age, gender, postcode, household car & bike ownership, private/company vehicle, income, employment type, # children, major life changes, etc) Administer questionnaire: § Test group: One40 William (building opened 2011; majority government tenants, with some private, retail & hospitality) § Control group: same or similar organisations at alternative sites (with less favourable PT & AT access/facilities) § Aim for 100+ respondents per group Analysis will be conducted using SPSS: § Data cleansing: check for errors, outliers; run descriptive stats; t-tests; check sample distribution, transform if necessary § Cross-tabulation: test group travel behaviour before and after relocation (Stradling et al. 2000) § Principal Components Analysis: psychological factors (Steg 2005) § Multiple Discriminant Analysis: analyse difference between test and control groups in current travel behaviour, psychological motives and habits. The key objective of this study is to understand the impact of active and public transport infrastructure and services at the workplace on commuter mode choice. This involves its: § impact on commuter behaviour § ability to disrupt habit and influence psychological motives § implications for future policy Many cities are seeking to shift commuters away from car use in favour of public transport and active transport (walking and cycling). A significant shift offers many advantages, including: § Reduced congestion (and associated costs) § Reduced emissions and better air quality § Improved health outcomes (including a reduction in major preventable diseases, such as obesity) Potential Implications Workplace conditions and employment practices are arguably easier (and more expedient) to influence than residential ones. If workplace (destination) factors: have a significant effect on mode choice; can disrupt commuter habits; and/or influence psychological motives for car use, this could inform policies to reduce commuter car use and its negative effects in cities. Literature Review Data Collection Will the below workplace (commuter trip destination) factors: § Increase the use of active and public transport for commuting? § Reduce commuter car use? § Disrupt the habit of commuter car use? § Affect psychological motives (affective/instrumental) for car use? Infographics created by the researcher based on cited source information.
- 16. ROAD TRANSPORT EMISSIONSAND ITS EFFECT ON PUBLIC HEALTH IN GHANA A CASE STUDY OF THE ACCRA PILOT BRT ROUTE Daniel Essel: Msc Transport Planning & Environment Supervisor: Dr. James Tate Co-supervisor: Jeffrey Turner Background A major problem facing the world today is road transport emissions which have been increasing at a much faster rate than anticipated. There is little evidence to support the fact that the current growth in vehicle ownership especially in developing countries will decline. Vehicle population in Ghana increased from 511,755 in 2000 to 1,591,143 in 2013 and projected to grow by 10% per annum. A roadside study reports high levels of PM10 exceeding the EPA- Ghana 24 hour mean of 70µgm-3 even though WHO limit value for PM10 is 50µgm-3. 79% of the samples collected at 3 roadside sites along the BRT route exceeded the EPA-Ghana 24-hour PM10 air quality guideline of 70 µgm3. Exposure to emissions at roadsides are 7 times higher within 15 metres but decay as distance increases. Epidemiological studies have confirmed short and long-term effect of vehicular emissions on respiratory related illnesses. Objectives Model current levels of vehicular emissions along the BRT route Assess air quality concentrations along the BRT route Assess its health implication on residents, traders and commuters along the BRT route Expected Outcomes Residents living within 150m from the BRT route would have higher exposure to traffic pollutants than those living further away The health implications will vary as traffic levels changes Commuter and traders spending longer hours along the BRT route will have higher exposure to traffic emissions References Driver and Vehicle License Authority, 2013: Unpublished Report of Vehicles Registered in Ghana Ebenezer Fiahagbe, 2012. Air Quality Monitoring in Accra, Ghana Kim, J.J. et al. 2004. Traffic-related air pollution near busy roads: the East Bay Children's Respiratory Health Study. American journal of respiratory and critical care medicine. Wright, L. and Fulton, L. 2005. Climate change mitigation and transport in developing nations. Transport Reviews Proposed Methodology Pilot BRT Route 24-Hour PM10 Concentration along the route Date: 2nd May, 2014 Extract of a section of the BRT route Proposed Methodology Source: Adapted from Google Maps Source: EPA Ghana- Air Quality Monitoring Programme
- 17. Student : DavidNunoo Programme : MSc. Transport Planning and Engineering Supervisor : Dr. Samantha Jamson Leeds – Bradford Canal Towpath Improvements: Will it encourage social and commuter cycling along the canal? Institute of Transport Studies Date : May 2014 Research questions: 1. Is perceived risk a serious obstacle to cycling and walking along the canal? 2. Is cycling and walking considerably affected by perceived risk along the canal? 3. Who the predominant users of the towpath are and their trip purpose(s)? Background The Department of Transport (DfT) granted Leeds and Bradford City Councils permission to implement a £29million ‘cycle superhighway’ between the cities. It is foreseen that this will improve the economy, environment, road safety and people’s health (Bradford-Telegraph-Argus, 2013). As part of the grand scheme, 14 miles of the existing Canal Towpath between Shipley and Armely is to be upgraded with high quality resurfacing. Figure 1: Location plan References 1. Bassuk, S.S. and Manson, J.E. 2005. Epidemiological evidence for the role of physical activity in reducing risk of type 2 diabetes and cardiovascular disease. Journal of Applied Physiology. 99(3), pp.1193-1204. 2. Bradford-Telegraph-Argus. 2013. £29 million 'Highway To Health' cycling road scheme announced. Bradford Telegraph and Argus. 3. Caltabiano, M.L. 1994. Measuring the similarity among leisure activities based on a perceived stress-reduction benefit. Leisure Studies. 13(1), pp.17-31. 4. Chapman, L. 2007. Transport and climate change: a review. Journal of transport geography. 15(5), pp.354-367. 5. Organization, W.H. 2009. Global status report on road safety: time for action. World Health Organization. Contact information • David Nunoo | Institute of Transport Studies, University of Leeds • Email: ts12dkn@leeds.ac.uk • www. leeds.ac.uk Proposed Methodology Research aims: 1. To determine if the improvement works along the canal towpath will result in an increase in the number of commuter and leisure cyclists along the route. 2. To determine if the improvement works will improve the safety perception of cyclists and pedestrians along the route. 3. To determine if there is an improvement in the cycling and walking experience along the route following the works. Figure 2: Existing section of the towpath Expected outcome It is anticipated that the improvement works of the Canal Towpath will result in a general increase in cycling and pedestrians activities along the route. Benefits of Cycling: 1. Cycling decreases the occurrence of ischaemic heart disease, cerebrovascular disease, depression, dementia, and diabetes (Bassuk and Manson, 2005). 2. Cycling reduces the occurrence of respiratory problems (Organization, 2009). 3. Cycling could reduce stress in individuals (Caltabiano, 1994) 4. Cycling is energy efficient because air emissions, noise pollution and greenhouse gases are not derived from it (Chapman, 2007).
- 18. BACKGROUND The private financefunctions in developing and expanding Manchester Airport Supervisor: Nigel Smith Dayuan Xu MSc (Eng) Transport Planning and Engineering Institute for Transport Studies University of Leeds Email: ts13dx@leeds.ac.uk Analyse the functions of private finance in those successfully extended airports. Analyse the risk of private investment and measures to reduce the risk. Identify the most effective mechanisms for utilising private finance in future Manchester Airport development. How to create a win-win model in PPP. Manchester Airport ranks only second to Heathrow airport in the UK. There are now three passenger terminals and two runways. The forecasts for Manchester suggest that the Airport could be handling some 38 million passengers by 2015 and the number could rise to around 50 million by 2030. Planning to provide an additional terminal to expand capacity and exploit economic benefits. FURTHER WORK OBJECTIVES METHODOLOGY Preliminary activities Design issues Limitations Obtain database of Manchester Airport. Risk analysis of different PFI forms on both ground and air sides. How to reduce risks in the PPP. What could Manchester Airport learn from the completely extended airports. The pros and cons of private investment on airport. Evaluate the private finance in airport development. Prepare Generate dissertation Data collectionAnalyse Manchester Airport Documents Archival records Interviews
- 19. Protest -characterised as beingnon violent they involve a collection of people who come together to protest a cultural, social, political or economic issue (Oliver, et al. 2012). Riot - Riots are one example of anti-government demonstrations which is a spontaneous outburst of violence from a large group of people (Barkan. 2012) Flashmob - Strangers meet at a predetermined public location, perform an unusual behaviour, and then disperse (Duran. 2006) Mediated Crowd - A new social phenomenon relating to collective action which emerges as a result of the virtual arena of ‘’Web 2.0’’ and new mobile technologies Web 2.0– Characterised as being a interactive social media and user generated content allowing users to exchange content The mobile criminal: Protests, Riots & Flashmobs Emma O’Malley Supervisor: Frances Hodgson Aim Understand how the communication aspect of social media can influence the organisation of Protests, Riots and Flashmobs, specifically those which occur on transport networks or are in response to changes on the network. Objectives • How is transport used as the stage andor reason or action? • Do changes in communication technologies, particularly social media significantly influence social organisation to initiate new forms of protests (e.g., flashmobs, critical mass) on the transport system? • Following acts on transport networks how do transport systems respond and recover? Background The world is made up of networks whether environmental, social or economic; this project looks at the links between communication networks and transport networks, specifically at how communication networks as part of new social media is used to support action which disrupts or is a result of changes to transportation system. Transport Networks • Transportation systems are often the focus of this action as it is intertwined with practically every aspect of human life meaning that: • Large numbers are people are affected by changes or disruption to the system whether on a local or global scale • Transportation networks are easily accessible • Action on the system will be very visible (Blickstein and Hanson. 2001). Communication Networks The creation of the mediated crowd is an example of how public communication practices have changed in the twenty-first century (Baker. 2012). Social Media allows everyone with access to have a voice and removes physical and spatial barriers allowing communication with a vast amount of people who may share the same mindset (Baker. 2012; Moler. 2013). This allows for a new form of social organisation where people can create or connect with social movements outside existing channels far quicker and easier than ever before (Bartlett. 2013). Preparedness It is important to understand how this new form of communication influences the organisation these forms of protest, especially those which occur spontaneously and have large negative impacts, as it can assist in preparedness and recovery from such events. As we move deeper into the ‘internet age’ it is important to understand mobility not just in the form of movement but also related to the ‘new mobilties perspective’ includes the movement of information through the use of the internet and media outlets (Sheller and Urry. 2006). New Mobilties Perspective Method Complete an in depth study on literature surrounding three main areas: • Mobility and communication • Networks (Transport, Communication) and how these networks influence each other • Existing policy to enhance ‘preparedness’ in the face of new forms of protest Conduct questionnaires with people who have been involved with protests and interviews with participants who took a leadership role in organising protests such as Critical Mass or the London Die- In. Major Case Study Critical Mass is an urban sustainability and cycling movement where once a month a large groups of cyclist ride through a city in rush hour in order to increase the visibility of cycling (Carlosson. 2002). The event is decentralised with no one leader, today the internet has allowed participation to increase, continue and transfer to other cities in a cheap and quick way (Blickstein and Hanson. 2001). Other Case Studies will include London Die-In, Plane Stupid, and London 2011 Riots. Glossary of Terms Expected Outcomes Identify to what extend new social media affects the organisation of social protests in the UK Establish what measures can be taken to reduce negative impacts of such protests and whether integrating new social media can help this aim
- 20. TRANSPORT IN DEVELOPINGCOUNTRIES (The benefit of implementing NMT Master Plan in Tema, Ghana) Emmanuel N. Tetteh: MSc Transport Planning and Engineering Supervisor: Jeff Turner 1. BACKGROUND Transport planning policies in many developing countries have followed the western systems by using of models such as Highway Development Management (HDM-4) which focuses on or mainly dominated by motorists transport. Hence the gap between motorist and NMT especially in Africa. Non motorists transport is the ideal mode of transport travel within cities. This due to the fact that they require less space, less energy as well as zero noise and air pollution . NMT enhance safety and also has direct link with health It is widely established, from current studies that a sustainable transport in terms of impact on areas such as social economy, environment is the choice mode of walking and cycling, the two major means of urban NMT. In developing countries NMT is recommended as most sustainable transport mode. 2. AIM The aim of this dissertation would be to look at some of the benefits that the City of Tema would gain from implementing the master Plan. 3. OBJECTIVES The main objective will focus on the following: Identification of general NMT benefits Congestion benefits Challenges in terms of infrastructure A critical review of why NMT in Accra did not work 4. METHODOLOGY Secondary data available in final submitted report of ministry of road transport and Highways of Ghana 2013 master plan for Tema would be the main source of data to be used for this research. The existing data will be used to access the relative benefits of promoting NMTs such as health. 5. PROPOSED SCOPE This research will be limited to the analysis of congestion and economic benefits after the implementation of NMT Master Plan in the City of Tema in Ghana. The research will also look at some challenges that will need to be addressed during the implementation in terms of infrastructure for Non Motorists Transport (NMT). Cyclist in Tema Congested road in Tema Google Map of Accra and Tema
- 21. Geographical Location of Study Area THE EFFECT OF AXLE LOAD ON THE TRANS WEST AFRICA HIGHWAY – A CASE STUDY ON THE AGONA JUNCTION TO ELUBO ROAD SECTION IN GHANA MSc (Eng) Transport Planning and Engineering OBJECTIVES Thestudy will generally seek to analyse the economic effect of strict enforcement of axle load control limits on transit trade and road investment. And will specifically aim to answer the research questions. METHODOLOGY EFFECT OF AXLE LOAD CONTROL REGIME Purposive Sampling Technique TARGET GROUP 1. Heads of Institutions /Senior Officers (GPHA, GSA & HAULERS) 2. Transit Trucks only GROUP 1 Personal Interviews using Questionnaires GROUP 2 Field Survey to Collect Axle Weights Secondary Data & Design Parameters of Case Study Design Scenarios for Sensitivity Analysis DATA ANALYSIS Exploratory and Confirmatory ECONOMIC VIABILITY HDM‐IV or CBA KEY FINDINGS, RECOMMENDATIONS AND CONCLUSIONS Source: National Overloading Control Technical Committee, South Africa (1997) EFFECT OF OVERLOADING OVERLOADING TREND IN GHANA BACKGROUND The West African Regional trading block, ECOWAS, is aligning its priorities towards economic integration of its member states. The development of the Trans West Africa Highway transiting five (5) member states (Cote D’Ivoire, Ghana, Togo, Benin & Nigeria) has been given the highest priority. 56% of this corridor lies within the boundaries of Ghana of which 20% is the case study area (i.e. Agona Junction to Elubo Road) A major threat to the life span of this road pavement is the axle weights of transit trucks. Transit trade is however a major contributor to Ghana’s Economy (World Bank, 2010). How to determine the balance of implementing an axle control limit that is viable for revenue generation at the port and prevent premature pavement deterioration. What is the current level and extent of axle overloading on the studied road? What is the design traffic loading used for the Agona Junction – Elubo road pavement design? What axle load limit will be economically viable to implement? DESCRIPTION OF STUDY AREA RESEARCH QUESTIONS PROBLEM STATEMENT A 110km road length along the coast of Ghana to the border with Cote D’Ivoire. Lies in the equatorial climatic zone and is the wettest part of Ghana. Nationally, it serves a population of about 1.84million inhabitants and an area of 23,921km2 (World Bank, 2010). Name: ERNEST O. A. TUFUOR (ID‐200661275) 2013/2014 Supervisors: JEFFREY TURNER AND DAVID ROCKLIFF Rutting Not Safe Source: Ghana Highway Authority, 2012 Annual Axle Load Report (2013) 2008 2009 2010 2011 2012 Number of Weighed Trucks 14625 47480 49586 140311 194516 Number Overloaded 3773 7026 9452 34302 34245 Percentage Overloaded 26% 15% 19% 24% 18% 26% 15% 19% 24% 18% 0% 5% 10% 15% 20% 25% 30% 0 50000 100000 150000 200000 250000 A MAP OF WEST AFRICA
- 22. Mode Choice Analysisfor Shopping Trips in Great Britain Gandrie R. Apriandito (200737853) Supervised by Jeremy Shires and Daniel Johnson • To examine the relationship between expenditure and transport accessibility • To identify what factors influence people in determining the choice of mode for shopping trips • To design relevant transport policy recommendations in order to get more people using bus instead of cars Objectives • Primary data was collected by ITS for DfT through an online survey across Great Britain • Distance and time travelled are compiled from Transport Direct to calculate journey cost Data Collection Methodology Primary Data Secondary Data Expenditure and Accessibility Mode Choice Analysis Regression Analysis Logit Model Transport Policy Recommendations • The dominant journey purpose for bus trip in Great Britain is shopping with 1.3 millions per annum, surpassing commuting purpose with 1.1 million passengers per annum (National Travel Survey) • Nearly 70% of shopping activities are located in either city or town centres. Bus service is essential in providing efficient accessibility to the potential demand • More than half of shopping trips are undertaken by cars Background Key Issues Logit Model Un,j = Vn,j + εn,j Example for single observation n with j different modes • U: Utility choice function • V: Deterministic function of the attributes • ε: Unobserved part (distributed independently and identically) Expenditure Model • It is the function of individuals characteristics and generalised cost • Relates to income, employment, shopping location, modes, specific purpose • Generalised cost is the function of accessibility and fares (for bus) Structuring a well-defined decision guidelines based on demand and supply characteristics of the traveller and alternatives available
- 23. The railway systemin Great Britain is the oldest in the world. The world's first locomotive-hauled public railway opened in 1825. Rail passenger demand has experienced significant growth in the last decade. The study is aimed at undertaking analysis to determine quantitative elasticity variations with key factors that drive passenger rail demand in Great Britain for the period 2002 to 20011. This information is vital in facilitating decision making, planning, management, policy formulation and investments in the transport sector. A measure frequently used to summarize the responsiveness of demand to changes in the factors determining the level of demand is the elasticity. Given as : Where ∆y is the change in the demand y, and ∆xi is the change in the explanatory variable xi. • The study offers valuable insights to the variations in the responsiveness of key drivers to rail travel growth. • There is plenty of empirical evidence on elasticity’s but not so much evidence on examining variations. The main aim of this study is to produce quantitative indications of elasticity’s variation with key factors such as distance; route; ticket type;. i. To find evidence on fare elasticity variations. ii. To find evidence on service quality elasticity variations iii. To determine general journey time elasticity variations iv. To find evidence on elasticity variation with the strength of competition v. To investigate evidence on GDP elasticity variations across routes , distance and overtime. • The study will adapt the conventional modelling approach, the fixed effect model (FEM) expressed as: • 𝒍𝒏𝑽𝒊𝒋𝒕 = 𝝁𝒊𝒋 + 𝜶𝒍𝒏𝑭𝒊𝒋𝒕 + 𝜷𝒍𝒏𝑮𝑱𝑻𝒊𝒋𝒕 + 𝜸𝒍𝒏𝑮𝒊𝒕 + 𝜹𝒍𝒏𝑷𝒊𝒕 + 𝜼𝒍𝒏𝑻𝒊𝒋𝒕 + 𝝀𝒍𝒏𝑪𝒊𝒋𝒕 + 𝝆𝑯𝒊𝒕 +𝜺𝒊𝒕 • The FEM allows the time invariant differences between flows which cannot be included or the time-invariant difference between flows to be expressed as a specific function of included variables as compared to the ratio modal approach. • The beauty of greater generality of FEM makes it preferable for estimation of panel data. • The basic model for the study is the fixed effect model (FEM) as opposed to the previous studies that used the ratio model (RM) and the PDFH. • Quantitative secondary panel data from rail operating companies will be used in this research, consisting of 184 flows ranging from 20 to 300 miles between stations, in 13 periods from 2002 to 2011. • Econometric analysis will be done using Eviews soft ware. Presentation of results in forms of figures, tables, charts • Output will be in two forms: o Within group variation: variation over time for each flow o Between group variation: variations flows AN ANALYSIS OF ELASTICITY VARIATIONS IN RAIL PASSENGER DEMAND IN GREAT BRITAIN 2002 -2011 • Resent developments in the field of elasticity’s have led to renewed interest in extending the analysis to variations in elasticity’s across different key factors. • Fares and quality of service are fundamental to the operation of public transport since they form major sources of income to operators. Evidence on fare elasticity and quality of service elasticity variations are crucial in decision making on pricing policy, service level changes and evaluation of non equal- proportional fare changes for cost effective schemes. • The last decade has seen transformation of the railway therefore, it is important for policy-making to be informed by best available knowledge about the variations in elasticity's • The GDP elasticity represents the positive impacts of economic activity on business trips and income on leisure trips. BACKGROUND WHY IS IT AN IMPORTANT SUBJECT? WHY ARE WE STUDYING IT? WHAT DO WE HOPE TO ACHIEVE? HOW ARE WE GOING TO DO IT? WHY THIS MODEL? WHAT EVIDENCE IS THERE? Gerald Harry Ekinu- MA Transport Economics (ID: 200734159) Supervisor: Professor Mark Wardman area; elapsed time and levels that this variables take • A need to recognize and address the limitations of previous/ current studies in the modelling approaches used.
- 24. The Role ofIncomes in Discrete Choice Models: implications in welfare measure in transport investment appraisal 1. Background, Motivation & Objectives 2. Theoretical Framework 3. Overall Methodology 4. Case studies: railways 5. Expected Results 6. References •Small, K.A. and Rosen, H.S (1981) “Applied welfare economics with discrete choice models’. Econometrica, 49 (1) 105-130. •Batley, I. and Ibanez, N. “Applied welfare economics with discrete choice models: Implications of Theory for Empirical Specification”. Working Paper. •Jara-Diaz, S.R. (2007). Transport economic theory. Oxford: Elsevier. •MaFadden, D. (1973)“Conditional Logit Analysis of Qualitative Choice behavior”. UNIVERSITY OF LEEDS Institute for Transport Studies (ITS) • Since the theoretical work of Small and Rosen (1981), applications in discrete choice models to welfare analysis in transportation sector have taken relevance in the academic and policy-makers grounds. • The mis-specifying of incomes in discrete choice models might potentially lead to inaccurate measures of welfare. • The theory in discrete choice models has made an important progress over years, that its recent approaches may cope potentially the mis-specifying of incomes in discrete models. • To examine the role of incomes in discrete choice models from: (1) the theoretical basis in welfare measure; and (2) practical application in investment transport appraisal. Literature Review Theoretical basis: to review the concepts stated in Batley and Ibanez (2010). Practical basis: to examine how incomes have been specified in DCM in literature. Cases study (a) Crossrail and (b) Linea 2: to review the way of incomes have (or not) been specified in the calculation of welfare. to analyse the assumptions regarding incomes in measuring user benefits. Report of findings Implications of mis-specifying incomes in welfare analysis. Outline a practical guidance of income effects in discrete choice models. • Incomes in discrete choice models might have implications for practical purposes in measuring welfare. • The implications in welfare measure of mis-specifying incomes in discrete choice models might be significant and lead to inaccurate calculations of benefits in transport investment appraisal under some circumstances. • Assumptions regarding incomes might be potentially more compatible with techniques in advanced discrete models. Marshallian Demand X=X(P,M) Hicksian Demand X=X(P,U0) P X1 M/P1 P0 P1 Subst. Effect Income Effect a b a + b = CS a = CV 𝑀𝑀𝑀𝑀𝑀𝑀 𝑈𝑈 𝑋𝑋 s.t. ∑ 𝑃𝑃𝑖𝑖 𝑋𝑋𝑖𝑖 ≤ 𝐼𝐼𝑖𝑖 ; 𝑋𝑋𝑖𝑖 ≥ 0 X2 X1 M/P0 A B M/P1 C U1 U0 𝐶𝐶𝐶𝐶 = − � � 𝑋𝑋𝑖𝑖 𝑐𝑐 (𝑃𝑃𝑖𝑖 𝑈𝑈0) 𝑑𝑑𝑃𝑃𝑖𝑖 𝑖𝑖 𝑃𝑃𝑃 𝑃𝑃0 Neo-classical approach of welfare Δ𝑀𝑀𝑀𝑀𝑀𝑀 = − � � 𝑋𝑋𝑖𝑖(𝑃𝑃, 𝐼𝐼) 𝑑𝑑𝑃𝑃𝑖𝑖 𝑖𝑖 𝑃𝑃𝑃 𝑃𝑃0 𝐶𝐶𝐶𝐶 = − ∫ ∑ 𝑋𝑋𝑖𝑖 𝑐𝑐 𝑃𝑃𝑖𝑖 𝑈𝑈0 𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖 𝑃𝑃𝑃 𝑃𝑃0 = 𝑒𝑒 𝑃𝑃0 , 𝑈𝑈0 − 𝑒𝑒(𝑃𝑃1 , 𝑈𝑈0) A theoretical approach of income effect in welfare measure Discrete choice models in demand Discreteness in demand can be modelled in at least three forms when goods may be (Small and Rose, 1981): (a) available in continuous quantities; but in only one mutually exclusive varieties, e.g. housing/rent; (b) available in discrete large units that one or two are chosen, e.g. transport modes; and (c) purchased because nonconcavities leads corner solutions, e.g.tv show aired simultaneously. To exemplify illustratively a probabilistic choice, a decision-maker faces the following task: Decision-maker 𝑃𝑃𝑃𝑃𝑘𝑘 = 𝑃𝑃𝑃𝑃(𝑤𝑤𝑘𝑘 + 𝜀𝜀𝑘𝑘 > 𝑤𝑤𝑘𝑘 + 𝜀𝜀𝑘𝑘)∀𝑖𝑖 ≠ 𝑘𝑘 𝑃𝑃𝑃𝑃𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡 = 𝑃𝑃𝑃𝑃(𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏 − 𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡 < 𝑤𝑤𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡 − 𝑤𝑤𝑏𝑏𝑏𝑏𝑏𝑏) 𝑃𝑃𝑃𝑃𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑃𝑃𝑃𝑃(𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡 − 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏 < 𝑤𝑤𝑏𝑏𝑏𝑏𝑏𝑏 − 𝑤𝑤𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡) Institute for Transport Studies MA Transport Economics FACULTY OF ENVIRONMET Presented by: Gian Carlos Silva Ancco - ml12gcs@leeds.ac.uk - May 2014 Advised by: Dr. Richard Batley Crossrail (London): £10.2 billion investment; 21km twin-bore tunnel, NPV £6.5 billion using DfT VoT or £11.5 billion using TfL VoT; increased capacity of London transport network; time saving DfT £7.4 bn or TfL £10.2 bn; congestion relief DfT £5.9 bn or TfL £8.1 bn; 200,000 passengers morning peak; discount rate 3.5 and 3.0; conventional BCR DfT 1.87 or TfL 2.55. Metro Linea 2 (Lima): USD 6.5 billion investment; PPP contract; 35km twin- bore tunnel; 4 to 6 year of construction; 35 stations; number of trains from 26 to 42; 662,346 estimated passengers daily; NPV USD 759 miles; discount rate 9%, BCR 1.15, VoT USD 2.51; max reduced journey time between two stations: 70 min. The income effect (variation in the purchase power) may be present in a lump-sum or change in price. The formulation of welfare is given by: Δ𝑀𝑀𝑀𝑀𝑀𝑀 = − ∫ ∑ 𝑋𝑋𝑖𝑖(𝑃𝑃, 𝐼𝐼) 𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖 = − 𝑃𝑃𝑃 𝑃𝑃0 ∫ ∑ − 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖 𝑃𝑃𝑃 𝑃𝑃0 If marginal utility of income (denoted by λ) is constant, then ∆MCS equals CV: Δ𝑀𝑀𝑀𝑀𝑀𝑀 = 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 𝑣𝑣 𝑃𝑃1, 𝑦𝑦 − 𝑣𝑣 𝑃𝑃0, 𝑦𝑦 = 𝑒𝑒 𝑃𝑃0, 𝑈𝑈0 − 𝑒𝑒(𝑃𝑃1, 𝑈𝑈0) The assumption of constant λ implies path- independency in Marshallian demand, i.e. alike welfare measure in Hickesian and Marshallian approach. Where: 𝜆𝜆 = 𝑑𝑑𝑉𝑉 𝑑𝑑𝑑𝑑 ⟹ 𝜕𝜕𝑥𝑥𝑥𝑥 𝜕𝜕𝑝𝑝𝑝𝑝 = 𝜕𝜕𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕𝜕𝜕 ∀𝑖𝑖, 𝑗𝑗
- 25. f f Site Profile: Merseyside Population:1,381,200 (9th) 645 km2 (43rd) 5 Boroughs – Knowsley, Liverpool, Sefton, St Helens, Wirral Valued at £21.9 Billion (2.1% of U.K economy) GVA (Gross Value Added) 0 1 2 3 4 50.5 Miles Ward_Boundaries No. of Households With Access To A Vehicles 18 - 94 95 - 171 172 - 247 248 - 323 324 - 400 401 - 476 477 - 552 553 - 628 629 - 705 706 - 781 No Data ¡ No. Of Households with No Access To A Vehicle (Inset of Wirral and Liverpool Boundaries) 0 2.5 5 7.5 101.25 Miles 0 1 2 3 4 50.5 Miles Ward_Boundaries No. Of Households with Dependents 8 - 28 29 - 49 50 - 69 70 - 89 90 - 110 111 - 130 131 - 150 151 - 170 171 - 191 192 - 211 No Data ¡ No. Of Households with Dependents in Merseyside (Inset of Wirral and Liverpool Boundaries) 0 2.5 5 7.5 101.25 Miles The Maps: The Maths: PC = TC TC + TT CIJ = a1.tIV + a2.tWK + a3.tWT + a4.tIN + a5.F + a6 The Method: Ai = Σ [BJf(CIJ)] Accessibility To Destination: This will demonstrate how accessible a location is based on either Thiessen polygons (zone inside polygon is closer to that sample) or isochrones (coloured bars of equal value). Modal Split/Destination Attractiveness: Multi-nomial logit models calculating modal split. (Equation can be used for destinations). These can be portrayed 3D or by colour (large spike, more accessible) Route Allocation/ Trip Chaining: Network Analyses using the cost equation above will show the “cheapest” route for an i n d i v i d u a l t o u s e . A l s o demonstrates trip-chaining (best way to carry out multiple tasks). D e m o n s t r a t e a n d evaluate disaggregate t e c h n i q u e s o f accessibility analysis Potential Issues Establishing parameters. How to measure a preference? How disaggregate is too disaggregate? Issues with deterrence functions. How viable are the methods? Literature Review: 1970 Geographical Space Accessible Space 2011 Hagerstrand developed a time-geographies concept. 3 M a i n C o n s t r a i n t s : “capability constraints”, “coupling constraints” and “authority constraints”. Computing power h a s a l l o w e d f o r disaggregate activity modelling to be c a r r i e d o u t . S t i l l a “ p i o n e e r i n g ” m e t h o d . B u f f e r s a r e predominant technique Not a spatially defined problem. – Large geographical space, small accessible space. Need to consider area mobility, individual m o b i l i t y a n d a r e a a c c e s s i b i l i t y. Economic status, car availability and physical or social preferences and limitations can affect how a person can or wants to travel. A Local Travel Plan (LTP) set up by the Local Authority and Merseytravel seeks to “develop a fully integrated and sustainable transport network... And ensures good access for all in the community” (Merseytravel 2000) The End Result: GIS outputs of varying types with two main aims: 1. Levels of accessibility for individuals demonstrating how their lifestyles affect their travel choices and access. 2. Differences between the disaggregate methods used in the study and aggregate methods carried out as a comparative tool. This will establish if the individual level studies are more accurate than aggregate measures, and if so, to what extent, with what outcomes? What Next…? Need to establish parameters to use within the functions. Carry out the GIS techniques and modelling. Analyse and evaluate the datasets – See what is possible. Large number of citizens in certain areas are not car users and as such find it hard to carry out fundamental tasks, such as taking children to school or going shopping. Thus creating and social exclusion transport poverty. f f Some Important Questions: Can GIS model the movements of individuals based on their preferences, motivations and restrictions, and how these relate to transport? Does a micro-level analysis offer a viable alternative to the current methods of study? One Size Doesn’t Fit All... Many relationships exist between different individuals preferences, capabilities and the levels of their t r a n s p o r t a c c e s s i b i l i t y . Aggregate methods (groups, not individuals) can miss important elements of a persons accessibility. Study Aims: Carry out an accessibility study of Merseyside at a disaggregate level. Highlight inefficiencies in aggregate modelling Show that individuals from the same areas have differing travel patterns.
- 26. Methodology Traffic signalsare important in the safe use of road space and efficient control of traffic in congested urban transport networks. Combining a traffic model and an optimisation method, traffic signal control models devise signal timings that meet certain objectives. The current traffic signal control is based on the average traffic condition, and does not account for variability (or say ‘noise’) in traffic. To develop a traffic signal timing model that account for variability in traffic. To consider Cross Entropy Method (CEM) with micro-simulation in the model. • To test the performance of the model in a realistic network. Background 3 Study4 Scenarios Objectives2 1 DRACULA micro-simulation modelling Get detailed information (delay, driving behavior, etc.) to appraise performance of each signal timing. Cross Entropy Method (CEM) Start of stage 1 Start of stage 2 1 2 3 4 56 Distribution function Select the best 5% solutions and update parameters of distribution through minimizing the Kullback–Leibler distance, which is equivalent to the program: 𝑀𝑎𝑥 𝐷 𝜇, 𝜎 = Max ln 𝑝 𝑥𝑖; 𝜇, 𝜎 𝑖 Where 𝑥𝑖 represents the best 5% solutions. Stop the iteration until the new values of parameters are equal (or close enough) to the previous one. Solutions Appraise Solutions Micro-simulation model Rank and select Update Best Solution Convergence ? Develop a Matlab code to implement the CEM model, providing input solutions to and taking results from DRACULA. Test the integrated model on a number of representative and realistic networks, and compare the results with standard signal timings. Test the performance on modelling different options (e.g. different CEM updating methods, number of simulation runs) Compare and contrast the restricts, draw conclusions and write report. Jialiang Guo - ml12j5g@leeds.ac.uk MSc (Eng) Transport Planning and Engineering Supervisor: Ronghui Liu May 2014 µ σ Roundabout Signal timing generation Generate a big sample of signal timings (say 1000) from a given distribution function 𝑝 𝑥; 𝜇, 𝜎
- 27. • Road pricing(tolling) dates back to the 17th century introduced after the turnpike Act – 1663 in UK and to 18th century in the USA; • Was predominantly used to raise funds for construction and maintenance of highways; • In recent times, tolling has been used for numerous reasons; • Implemented in Singapore since 1975 as a traffic management tool; • In London, it has been used since 2003 to reduce congestion and protect the environment; • Norway, Sweden and Malaysia use road tolling to raise funds for road transport budget support. Road Pricing: A Case of Competing Private Road Toll Operators The study intends to: • Study techniques of identifying Nash Equilibrium for multiple toll operators; • Examine toll levels that ensure private operators maximise revenue and meet the Nash Equilibrium conditions; • Establish how revenue maximising tolls compare with social welfare maximising tolls. By Jonah Mumbya | Supervisor – Mr. Andrew Koh | Second Reader – Dr. Chandra Balijepalli Introduction Objectives Motivation Effects of Increasing Congestion Environmental Costs of Traffic Government Budget Constraints • Global costs of congestion are high and projected to increase with increasing traffic delays; • In UK, congestion costs (due to delay) stood at £20bn in 2000 and projected to increase to £30bn by 2020; • NTM projects traffic to grow by 43% as a result of a 66% GDP growth from 2010- 2040 in England alone; • This would lead to congestion increasing by 114% and lost seconds per mile would increase by 36% hence cost as travel speeds would reduce by 8%. • Transport is the third largest contributor to global warming just behind energy (electricity and heating) and industry; • In UK, Road transport contributed over 27% to Green House Gasses with cars having 58% of this in 2009; • With increasing motorisation, this is likely to be the same or worse with time. • Governments are continually getting constrained to finance road infrastructure using traditional budget appropriations; • Hence road users ought to meet part of the road infrastructure investment costs/budgets; • Road pricing supports about 32% of Norway’s national road system budget and 46% of Spain's road budget. Methodology a) Link Selection; Link selection shall be based on the difference between link marginal cost and average cost and the level of congestion of the do-nothing scenario. b) Determine Tolls; Iteratively, tolls will be set until a Nash Equilibrium is achieved for the competing toll operators. c) Traffic Assignment; Using SATURN, traffic shall be assigned to the network based on Wardrop’s first equilibrium principle. d) Calculation of Revenue and Benefits. Based on assigned link flows from SATURN, revenues and social benefits will be calculated. Test Network – Edinburgh 1 2 3 4 5 Select Links [SATURN] Set Tolls Assign Traffic to the Network [SATURN] Is Nash Equilibrium Achieved? Calculate Revenue and Social Benefits No Yes
- 28. INVESTMENT DECISIONS FORRESILIENT TRANSPORT INFRASTRUCTURE: A CASE STUDY OF THE DAWLISH RAILWAY LINE COLLAPSE Kwame Nimako: MSc Transport Planning and Engineering (2013-2014) Supervisors: Prof. Greg Marsden and Prof. Nigel Wright 1. BACKGROUND • A good transport system promotes the movement of people, goods and services from one point to another under normal conditions (Amdal and Swigart, 2010). A nation’s economic vitality to a large extent depends on its transport network (Amdal and Swigart, 2010). • The occurrences of natural disasters such as flooding, make transport networks such as railway lines vulnerable (Doll et al., 2013), thereby impacting negatively on train services. • For the disruption at Dawlish, the Train Operating Companies will be paid £16 million by Network Rail for lost revenue over the period (BBC, 2014). • As the frequency and magnitude of such disruptive events become more probable in future due to climate change, the cost of providing engineering interventions required for reliable transport services increases significantly. • Since most transport infrastructure are long term assets (Doll et al., 2013), there is the need for adequate investment decisions on cost effective strategies to be employed to enhance their resilience over their life span. 2. AIM The aim of this dissertation is to develop a methodology to be utilised in making cost-effective investment decisions to improve the resilience of railway lines to disruptions. 3. OBJECTIVES To achieve this aim, the following objectives have been set: i. Understanding how demand for transport changes during a major flooding event ii. Estimating the impacts of the resultant disruption on users of the infrastructure iii. Collecting estimates of alternative flood risk mitigation investments iv. Developing a methodology to assess the cost- effectiveness of such investments under different future scenarios of flood risk Great Western Rail line - London-Exeter-Dawlish-Plymouth-Penzance. (Source: First Great Western network map) Location of Dawlish and the Railway line Disruption (Source: Google.com) 4. PROPOSED METHODOLOGY 5. EXPECTED OUTCOME It is anticipated that this study will produce an Investment- Frequency Matrix based on current and future scenarios of disruptions to be utilised to improve the resilience of railway lines. 6. REFERENCES • Amdal, J.R. and Swigart, S.L. 2010. Resilient Transportation Systems in a Post-Disaster Environment: A Case Study of Opportunities Realized and Missed in the Greater New Orleans Region, 2010. • Doll, C. et al. 2013. Adapting rail and road networks to weather extremes: case studies for southern Germany and Austria. Natural Hazards. pp.1-23. • British Broadcasting Corporation. 2014. Storm-hit Dawlish rail line compensation payout revealed. [Online]. [Accessed 28 April 2014]. Available from:http://www.bbc.co.uk/news/uk-england-devon- 27055780.
- 29. University business travelchoices andUniversity business travel choices andUniversity business travel choices and working practices Kanintuch Siripaibool Supervisor: Ann Jopson (ts13ks@leeds.ac.uk) working practices Kanintuch Siripaibool Supervisor: Ann Jopson (ts13ks@leeds.ac.uk) Objectives Background Objectives • Find out the chosen / preferred travel mode choices of Nowadays there is an increasing in number of business • Find out the chosen / preferred travel mode choices of traveling; trips particularly for academic staff in the UK traveling to European cities and beyond. Most of staff traveling by traveling; • Compare the choices e.g. air vs rail between UK and European cities and beyond. Most of staff traveling by train or air depending on their preferences and time used. • Compare the choices e.g. air vs rail between UK and European cities; train or air depending on their preferences and time used. European cities; • Discover the advantages / disadvantages of each The aim is to understand the academic travelers preferred choices and the reasons behind that, the activities they • Discover the advantages / disadvantages of each choice; choices and the reasons behind that, the activities they are doing while traveling and also how to reduce • Determine the activities they do while traveling and the are doing while traveling and also how to reduce environmental impact e.g. carbon emission (Carbon Management Plan, 2011). influence to the chosen mode. Management Plan, 2011). Business Travel VS Business Travel • Average annual trips of educational staff (all modes) = VS • Average annual trips of educational staff (all modes) = 50 trips (Wardman et al., 2013); • 82% of business travelers said they spent some/most of time working while travelling (Lyons et al, 2008). MethodologyIn-vehicle VOT for Trip > 50km Methodology • Mixed method questionnaire used in the study (mainly35 In-vehicle VOT for Trip > 50km • Mixed method questionnaire used in the study (mainly qualitative) via online; 25 30 • Tick-box questions for the first part of questionnaire e.g. 20 25 Background, preferred choices; 15 20 £/hr • Open-ended questions for opinions about choices, advantages / disadvantages, etc. and some follow 10 15 advantages / disadvantages, etc. and some follow interviews for in-depth questions;0 5 interviews for in-depth questions; • Find out the repeated similarities / themes in data 0 Car Bus Rail Air • Find out the repeated similarities / themes in data collected. Car Bus Rail Air Norway’s 1997 data collected.Norway’s 1997 data University business travel choices andUniversity business travel choices andUniversity business travel choices and working practices Kanintuch Siripaibool Supervisor: Ann Jopson (ts13ks@leeds.ac.uk) working practices Kanintuch Siripaibool Supervisor: Ann Jopson (ts13ks@leeds.ac.uk) Scope Find out the chosen / preferred travel mode choices of Scope • Focus on trips involved with meetings/conferences, Find out the chosen / preferred travel mode choices of • Focus on trips involved with meetings/conferences, away from usual working place; Compare the choices e.g. air vs rail between UK and • Target only ITS staff, sample size: 25 – 30; Compare the choices e.g. air vs rail between UK and • Travel choices between Leeds and key European cities Discover the advantages / disadvantages of each e.g. Paris, Brussels, Amsterdam; Rail network in Europe Discover the advantages / disadvantages of each Rail network in Europe Determine the activities they do while traveling and the influence to the chosen mode. VSVS Source:Wikipedia (2011) Initial Findingsmethod questionnaire used in the study (mainly • Trips less than 500km air travel rarely used while trips method questionnaire used in the study (mainly more than 1000km air travel is predominate (Borken- Kleefeld et al., 2013); box questions for the first part of questionnaire e.g. Kleefeld et al., 2013); • Business travelers have high in-vehicle VOT than Background, preferred choices; • Business travelers have high in-vehicle VOT than commuting and leisure travelers (Wardman, 2008); ended questions for opinions about choices, advantages / disadvantages, etc. and some follow-up commuting and leisure travelers (Wardman, 2008); • Business travelers have high willingness-to-pay advantages / disadvantages, etc. and some follow-up depth questions; • Business travelers have high willingness-to-pay (Carlsson, 1999); depth questions; Find out the repeated similarities / themes in data (Carlsson, 1999); • Business travelers are mostly time-restricted, don’t strive Find out the repeated similarities / themes in data • Business travelers are mostly time-restricted, don’t strive for low price (Jung and Yoo, 2013).for low price (Jung and Yoo, 2013).
- 30. Pedestrian Safety forthe Visually Impaired and Elderly: Case Study: Leeds Background Aim Scope Pedestrians are often referred to as vulnerable road users, borne out of the fact that they comprise over 20% of those killed on the roads(WHO, 2013). The elderly and visually impaired pedestrians are more vulnerable in this context as they find it difficult to meander on the roadway as other pedestrians do. They usually fear the risk of being run over by vehicles coupled with being endangered by other roadway hazards like tripping to a fall on barriers like litter bins, concrete and sign post. This results in low level of confidence and hence suppressed mobility. This research aims at restoring confidence to the visually impaired and elderly pedestrians on using the roadway so they can get out more. The survey method will be used where qualitative data will be obtained from an in-depth face to face interview of 30 to 50 groups of visually impaired and elderly pedestrians using open ended questions, which will reflect the recipients’ feelings about their safety as pedestrians. Finally data will be analysed using content analysis of descriptive and interpretative measures where data will be sorted and classified into similarities and differences, and finally summarised and tabulated. Methodology The research is focused on the suppressed mobility of visually impaired and older pedestrians within Leeds. it will seek to identify what makes them vulnerable, as well as infrastructure and facilities available to encourage movement leading to their restored confidence and enhanced safety. Objectives • To understand the extent to which the visually impaired and the older people’s mobility is suppressed, as compared with the population as a whole. • To establish whether there is a link between suppressed mobility and the level of confidence in using the pedestrian and built environment; and if so, the nature of that link. • To explore possible ways of building confidence amongst the visually impaired and older people, leading to their enhanced mobility. By Jennifer Kuka Upuji Student ID: 200749910 Supervisor: Mr Bryan Mathews Implications 2 million people in the UK are living with sight loss. These figures are expected to rise to over 2,250,000 in 2020 (Fight for Sight, 2014) Zifotofsky et al,2009 http://Safetysigns-mn.com Department for Transport,2013 Pedestrian Accidents- > 60yrs Fhwa.dot.gov www.bhatkallys.com http://Clacksweb.org.uk www.sensors/special_issues/vehicle-control www.nei.nih.gov/eyedata/lowvision http://srsc.org.sg http://phillymotu.wordpress.com
- 31. Understanding travel behaviourto stadium and arena events : A Cardiff case studyLaura Crank, MSc Transport Planning (FT) Supervisor: Bryan Matthews The importance of travelling to events The car is the most popular mode of transport to stadium and arena events, and as the attendance of such events increases, it is noted, “…the demand for travel is heavily constrained both in time and space” (Robbins et al. 2007:303). High demand in a short space of time leads to congestion on the roads and overcrowding on public transport. Providing for such temporary ‘peak’ crowds would leave the additional infrastructure and services underused for the most part, which is economically unviable (ibid:304). The story so far… Obtained at least 150 responses between face-to-face and online surveys Met with Cardiff Council’s Operations Manger of Major Projects (Infrastructure) at one of the venues during an event. Managed to discuss issues at hand and how the council managed the city during large events Methodology Design survey taking into account aims and objectives. Test survey to ensure it is understandable and quick to complete Sample a range of events to capture different socio- economic groups Conduct face-to-face surveys at both Millennium Stadium and Motorpoint Arena, and promote online survey to gain 200+ responses Differentiate between results for each event, establish differences in audiences, distances travelled, travel habits Conclude what would need to be done to encourage modal shift to public transport Aims and Objectives To understand travel behaviour to events, developing on previous research at other stadiums and arenas It is becoming a growing interest to understand the behaviour of spectators and to determine what changes would have to occur to the public transport system to increase attractiveness and modal share of public transport to events. Research Questions What is the percentage of different modes of transport used to access events in Cardiff? What factors influence mode choice to events in Cardiff? Are there differences between mode choices to the two venues? Are different event audiences more “sustainable” in their travel choices, or willing to change their event travel habits? What changes would have to be made to public transport to increase its usage during events in Cardiff? References Robbins, D. et al. 2007. Planning Transport with Special Events: A Conceptual Framework and Future Agenda for Research. International Journal of Tourism Research. 9. Pp. 303-314. Yeates, J. et al. 2009. Changing Travel Patterns of Arena Visitors: Transportation Demand Management for Urban Arenas and Stadia. [Online] Available at: http://www.ite.org/Membersonly/techconference/2009/CB09C3001.pdf [Accessed April 2014] Washington: Institute of Transport Engineers. Modal split before and after TDM measures Location of venues and public transport stations in the vicinity of the Cardiff region A survey of stadium travel in New York found that after transport demand measures (TDM) were implemented, there was a decrease in car travel to events, whilst public transport use increased.
- 32. BACKGROUND Realistic drivingbehaviour models require detailed trajectory data for calibration; however, such data is very expensive to collect and process. Spatial transferability of driving behaviour models leads to significant saving of costs and saves time for the new location. RESEARCH QUESTIONS What driving behaviour models could be developed for two contexts within the same geographical areas? Which of the models would be recommended for transferability? To use data from two sites to develop three different driving behaviour model structures. To evaluate transferability of each of the three models using transferability scores. To make recommendation on the best transferable driving behaviour model. OBJECTIVES STUDY LOCATIONSITE 2 Transferability of Gap-Acceptance Driving Behaviour Model Student: Ahabyona M. Evelyn. Supervisor: Dr. Charisma C. Choudhury. 2nd Reader: Dr. Ronghui Liu. To test proximity effect on model transferability two are chosen from the same geographical area while the other data set is from a different geographical location. Transferability of data sets will be analysed using the likelihood-ratio test methodology. A micro-simulation tool (biogeme) will be used to predict the aggregate gap-acceptance behaviour of drivers. METHODOLOGY POTENTIAL RISKS Data being used may be out dated. Unrevealed factors such as bad weather conditions that may have affected data collection. The data may not be detailed enough to capture all aspects of gap-acceptance driving behaviour since it was collected for a different purpose. A DRIVER CHANGING LANES SITE 1 DATA COLLECTION
- 33. Understanding Free BusTravel in West Yorkshire Using Smartcard Data Context Methodology • Temporal variation of trip frequency i.e. by time of day or day of the week. • Influence of age on trip frequency. • Spatial variation of trips frequency i.e. by location, with influence of income/level of deprivation. Expected Findings MSc Transport Planning Dissertation Mmoloki S. S. Baele e-mail: ts13msb@leeds.ac.uk Scope and Objectives Scope • The study covers concessionary bus travel for WY only. • The focus is on after-scheme cross sectional travel data. Objectives of Dissertation • To determine the level of ENCTS pass use in West Yorkshire. • To determine trip frequency patterns according to temporal, spatial and socio-demographic variations i.e. by day of the week, location, income group, age, gender and disability type. • To evaluate value of smartcard data in determining travel patterns and the implications of the results on the policy for the ENCTS in West Yorkshire. LITERATURE REVIEW •Reviewing literature on concessionary bus travel, previous studies on smartcard data and complementary data sources. •Determine the relevance of literature to the study. DATA COLLECTION AND PREPARATION •Deciding on relevant data types and variables. •Extraction from WY Metro smartcard database – primary source. •Downloading relevant complementary data (e.g. census and NTS data). •Cleaning and organising data. DATA ANALYSIS •Defining trip data units of analysis and method of analysis from smartcard data. •Determining trip frequency distribution and statistical analysis e.g. regression analysis. •Comparison with other data sources (e.g. NTS). INTERPRETATION OF RESULTS •Identifying notable travel patterns. •Evaluating the relevance of results to policy. •Reflecting on the strengths and limitations of study and possible future areas of study. West Yorkshire Concessionary Free Bus Travel •West Yorkshire Passenger Transport Executive (WY Metro) issues smartcard type Concessionary Bus Travel passes as part of the English National Concessionary Travel Scheme (ENCTS). Senior Pass •Issued to permanent residents of West Yorkshire (WY); free off-peak bus travel for persons that have reached retirement age. Blind and Disabled Passes •Free travel on buses for blind persons at any time of day in West Yorkshire and off-peak throughout England and free, off-peak bus travel for disabled persons throughout England. Companion Pass •For persons accompanying eligible persons who are not able to travel alone. 67.3% 31.1% 1.6% Proportion of Trips by Pass Type Senior Disabled Companion 0 200 400 600 800 1000 1200 1400 Sun Mon Tue Wed Thu Fri Sat No.ofTrips Average Weekly Trips - March 2014 Companion Disabled Senior 0 20 40 60 80 100 120 0.00 0.50 1.00 1.50 2.00 2.50 3.00 Passholders Age Trips/person/week Mar’14 Average Trip Frequencies by Age All pictures under WY Metro Copyright May 2014
- 34. 2) Description ofthe Topic - Objectives In the context of this survey, new stated preference data on mode choice are collected, with two components: one using current settings and one incorporating reducing car use & switching to public transport commuting instead. The work then investigates the performance of models estimated on the base scenario to predict the behaviour after the incentives have been brought in, and looks at the benefits that a treatment of attitudes brings in this context. 3) Scope of the Research We investigate the response of the participants at 4 different scenarios - policy interventions: •Increase in fuel price •Increase in parking cost •Decrease in public transport cost (fare) •Increase in the frequency of public transport 4) Methodology Statistical Model: Discrete Choice Modeling - Integrated Choice & Latent Variable Model (ICVL) We consider that the interviewees have 3 transportation options: drive & pay for parking, drive & search for free parking & use public transport. We give them eight current alternatives in which the attributes of the three options differ slightly. After that, we introduce 4 hypothetical policy interventions from their base scenario, where only one attribute changes every time. The individuals’ choices depend on the perceived utility of each option, which itself depends on the attributes of the specific alternative (access time, frequency, travel time, finding space, egress time, cost), the person’s socio-demographic characteristics (age, income, gender, education, area of residence, marital status), but also on the latent attitudes an of the individual (pro-intervention attitudes). Those attitudes explain his responses to the attitudinal questions (indicators I). 5) Data Collection: Survey - Stated Preference Data Collection – Devision of on-line Questionnaire The questionnaire consists of four parts: • Existing Situation •Stated Preference Part → Before/After Policy Intervention Choices • Car/Public Transport Attributes & Attitudes • Socio - Demographic Characteristics Sample: Staff of the University of Leeds, size of approximately 100 persons who commute by car. On-line survey instead of face-to-face interviews Indicators I → Responses to attitudinal questions Socio-demographic Characteristics of interviewees Utility of car & public transport Attributes X of modes (car & public transport) Choice ↔ Probability of different scenarios occurring 1) Introduction-Background Nowadays, the challenge in transportation is to move to a more sustainable, environmentally friendly & economic way of commuting. To achieve this goal, incentives should be given to individuals in order to switch from car use to public transport. This dissertation aims to deepen in this issue, to investigate & quantify those incentives & examine all its parameters, as there are no retrospect surveys on this subject. Marina T. Triampela, Stephane Hess
- 35. Institute for TransportStudies Faculty of Environment 1INTRODUCTION Transport Sector Contribute to GhG Emisssion According to the House of Commons Environmental Audit Committee, “carbon emissions from transport since 1990 have moved spectacularly in the wrong direction – in marked contrast to other sectors”. Contribution of transport sector to total GhG emission increase to around 25% Road transport carbon emissions proportion on the transport sector emission is 75%-85%. Current CO2 emission of road transport are hovering at the same level as in 1990. Target of transport CO2 emission is 31% lower than 1990 base year by 2020 (CCC, 2013) Road pricing could be alternative mitigation to reduce carbon emission further. But many of current schemes tend to focus on congestion as their primary objective rather than look at the joint problem of tolling for congestion and emission. How to Calculate Carbon Emission? • DfT Methods (DfT, 2011) Where: Ce= carbon emission, L=Fuel consumption, Ceβ= Carbon emission per litres burnt, V= Average speed and a,b,c,d= Parameters based on “New UK Road Vehicle Emission Factors Database” • Alternative Models (Shepherd, 2008) 1. Simple fixed rate Apply average speed of network into equation of complex emission model below, and get constant addition of CO2 per trips 2. Complex emission models Where: g = CO2 emitted, V = average speed of link-based Then carbon emission can be monetized by multiply it with £70 per tonne of carbon emitted Cordon Pricing 4METHODOLOGY Researcher: Naf’an Arifian (ml12n2aa@leeds.ac.uk) --MSc. Transport Planning Supervisor: Simon Shepherd (S.P.Shepeherd@its.leeds.ac.uk) Second Reader: Dave Milne (D.S.Milne@its/leeds.ac.uk) How Effective are Cordon and Distance-Based Pricing at Reducing CO2 Emission? 2OBJECTIVES 1. To investigate welfare benefits of road pricing schemes taking into account CO2 emission and congestion 2. To investigate differentiation between simple fixed rate emission model and complex emission model dependent-speed. 3. To investigate impacts of cordon pricing and distance based pricing policies on reducing CO2 emission Build Networks on SATURN OD matrix, road networks data and demand elasticity Develop Road Pricing Schemes Charges all-links (first best pricing adjust to include CO2 emission cost), Cordon and distance-based pricing by modified ‘generalized cost’ equation of SATURN Run SATURN and Record Outputs Link-based speed and flows Calculate and Investigate Emission cost of simple vs complex emission models, Congestion and emission cost of cordon vs distance- based pricing Comparison between Scenarios with First-best Pricing In terms of congestion cost, emission cost and welfare benefit What is Road Pricing? •Road pricing are direct charges for the use of roads, including road tolls, distance or based time charges, congestion charges particularly to discourage use of certain class of vehicles, fuel sources or more polluting vehicles. 1. First-best pricing; charges on all links in order to achieve maximum social welfare 2. Second-best pricing; under constraints to find optimum toll in order to achieve maximum welfare Ce= L*Ceβ 3 LITERATURE REVIEW Calculate and Investigate Welfare benefit of Cordon vs Distance-based pricing (by Plotting optimal toll take account (i) congestion, (ii) congestion + CO2 emission simple fixed rate model and (iii) congestion + Co2 emission complex model) Source: Shepherd (2008) Source: DECC (2014) Source: Shepherd et.al (2008) 1. Committee on Climate Change (CCC). 2013. Fourth carbon budget review – technical report : Sectoral analysis of the cost-effective path to the 2050 target 2. Department for Transport (DfT). 2011. The greenhouse gasses sub-objective: TAG 3.3.5 3. Department of Energy and Climate Change (DECC). 2014. Total greenhouse gass emission from transport 4. Shepherd, S. 2008. The effect of complex models of externalities on estimated optimal tolls 5. Shepherd, S., May. A., and Koh. A. 2008. How to design effective road pricing cordons References
- 36. www.kliaekspres.com www.ktmkomuter.com.my www.ktmb.com.mywww.prasarana.com.my www.spad.gov.my • Population ~29m • Capital: Kuala Lumpur • GDP per capita: ~10k USD • Age group [16-64] expandingKuala Lumpur Metropolitan Area (KLMA) www.wikimedia.com • Rapid motorization rate – Car Ownership at ~320 cars / 1000 per. • Fuel subsidized – Oil producing country - Approved Price / Floating Price Mechanism : Pump price lower/equal than actual market value. • Train Services: Intercity and Urban (KLMA) available Components Description Key Question Deduction of constant elasticity Method: Urban: Trips Made Intercity: Kilometer-Passenger Urban: Trips Made Intercity: Kilometer-Passenger Online Survey - Considering type of trip, gender, income, location. Dispersion question: Given a situation is that the fuel price has gone very high and is no longer affordable. If you had to make a leisure trip (visiting family/friends, holiday etc) . If petrol prices are too high, how do you travel? A) Train B)Bus Key Question: Deduction of the cross elasticity of rail demand with respect of pump car-fuel price. Area and Mode Area of study will be in Malaysia (East and West). Related modes: Train and Cars. Impact of Car Petrol Prices on Rail Demand: The Case of Malaysia Nik Mohd Rafiq Bin Wan Ibrahim MSc. (Eng.) Transport Planning and Engineering ts13nmrb@leeds.ac.uk Supervisor: Prof. Mark Wardman Institute for Transport, University of Leeds Quick Overview: Deduction of the cross-elasticity of rail passenger demand in Malaysia with respects to car petrol prices. TrainCar rainRidershipT arRidershipC iceCarFuelCariceCarFuelRail V V ,Pr,Pr, || 1. Situation Appraisal 3. Scope 5. Methodology / Data Collection 4. Literature2. Motivation iceCarFuelRail Pr, iceCarFuelCar Pr, rainRidershipTV arRidershipCV TrainCar, Urban Rail (Kuala Lumpur Metropolitan Area) Nationwide Intercity Rail (Nationwide) • Interesting to see in a fuel-subsidized country, where the actual transport fuel cost is not paid by the user, would there be any significant mode shift. • As oppose to EU or UK, whereby taxation of fuel is a national income stream, the effects of increase modal- shift to trains (public transport in general) in countries where fuel are “costly” to the nation, would be a point of interest. • Trending projects to improve in urban rail especially in KLMA and intercity rail services (Double Track, HSR) • Subsidy will be removed? Acutt and Dodgson 1996, Cross elasticities of demand for travel, Transport Policy Vol 2, pp. 271-277 Train Type Year Cross Elasticity w.r.t car-fuel price InterCity, NSE, Regional 1992/93 0.041 – 0.094 Underground 1992/93 0.017 Train Type Year Cross Elasticity w.r.t car-cost Inter Urban - 0.59 Urban - 0.25 Paullney et al 2006, The demand for public transport: The effects of fares. Quality service and car ownership, Transport Policy, 13(4), pp. 295-306 40.0 50.0 60.0 70.0 80.0 90.0 100.0 110.0 120.0 130.0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Consumer Price Index (2000 = 100) (Source DOSM) 0 2000 4000 6000 8000 10000 12000 14000 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Retail Fuel Sold [million liters] (RON97 + RON95) (Source KPDNKK) 0 20000000 40000000 60000000 80000000 100000000 120000000 140000000 160000000 180000000 200000000 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2001 2003 2005 2007 2009 2011 Passenger Ridership Urban Rail (Source: EPU) 0 200000000 400000000 600000000 800000000 1E+09 1.2E+09 1.4E+09 1.6E+09 1.8E+09 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Railways, passengers carried (passenger-km) [Source: DOSM] MALAYSIA Objective: To investigate the relationship of car fuel price to rail demand. Increasing of car fuel price (either due to global market or government’s removal of the subsidy) should hypothetically increase train patronage. 1. Literature Review on global PED 2. On-line Survey: Stated Preference 3. Regression from existing data: Cross elasticity is calculated using an equation that consists of several components. These components are partly review from literature to compare other international values followed by a re-calculation using method described below. A crucial part of this study is an online survey to deduce the diversion factor; non- payable option of cost car-fuel is hypothesized. (Source: DOSM) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1980 1984 1988 1992 1996 2000 2004 2008 2012 Pump price for gasoline (US$ per liter) (Source: World Bank) 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 0 500 1000 1500 2000 2500 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Length of railway track (km) (Source: DOSM) Roads, total network (km) (Source: World Bank) Track(km) RoadNetwork(km) - 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 1980 1986 1992 1998 2004 2010 Cars Motorcycle Other Vehicles Goods Vehicle Bus Taxi and Private Hire Mileage travelled Deduction from Retails Fuel Sold – considering fractioning to cars and yearly fuel consumption, calibrated with VehicleKilometerTravelled (recent Survey) Pump Fuel Price Considering Consumer Price Index Registered Vehicles over time
- 37. Background How Good theSATURN Model Representing the Network Impact of Lendal Bridge Closing Trial? Objective Scope of Study • Study area confined by the ring road (A64 and A1237) • Access crossing Ouse River A. Rawcliffe Bridge B. Clifton Bridge C. Lendal Bridge D. Ouse Bridge E. Skeldergate Bridge F. A64 Data • Survey Manual Classified Counts Survey has been conducted every autumn (September, October, November) Record bridge crossings activity for 12 hours (07:00-19:00) Data available motorised vehicle, bicycles and pedestrian However exact survey location is unknown • Automatic Traffic Count Data ATC location spread around the study area Record vehicle, cycle and car park • Network model and Trip Matrix of York City • Journey time data from Trafficmaster • However, data only limited to traffic count and journey time without specific detail to each vehicle (GPS or Number Plate) • Lendal Bridge Closing Trial 27th August 2013 until 26th February 2014. 10:30 until 17:00 closure limited to cars, lorries and motorbike (www.york.gov.uk/) • SATURN (Simulation and Assignment of Traffic to Urban Road Networks) Transportation network analysis program developed at Institute for Transport Studies, University of Leeds Generalised Cost, 𝐶 = 𝛼𝑇 + 𝛽𝐷 (α = PPM and β = PPK) while T and D denotes travel time and distance is the basic principle of route choice Combined assignment and simulation model Methodology Data Description Pujas Bakdirespati ts13plb@leeds.ac.uk (200792978) MSc. Transport Planning and Engineering 0 100 200 300 400 500 600 North 2012 North 2013 24 Hour Bridge Traffic Load Proportion Lendal Bridge Traffic Flow ATC Data Difference 2013 to 2012 Before the Closing Difference York Network Supervisor: Dr. David Milne 21.91% 11.46% 7.29%8.83% 13.74% 36.77% 22.08 % 12.00% 4.94% 7.87% 14.65% 38.44% 22.81 % 12.51 % 3.47 %8.15% 15.57 % 37.49 % 21.25% 11.42% 6.63% 7.56% 13.59% 39.54% Rawcliffe Bridge Clifton Bridge Lendal Bridge Ouse Bridge Skeldergate Bridge A64 2012 2013 Bridge Open Bridge Closed -16.00% -11.00% -6.00% -1.00% 4.00% 9.00% 14.00% 19.00% Direction 1 Direction 2 -16.00% -11.00% -6.00% -1.00% 4.00% 9.00% 14.00% 19.00% Direction 1 Direction 2 0 100 200 300 400 500 600 South 2012 South 2013 Lendal Bridge Select Link Assignment 11:00-17:00 Peak • To compare route choice between the result of SATURN model and observation data, which in this case the comparison could be divided in two steps: Route choice before the closing of Lendal Bridge trial, where the SATURN model will be calibrated with journey time and flow data in order to better reflect the condition Route choice after the closing of Lendal Bridge trial, where the SATURN model is slightly modified at Lendal bridge where it is banned in two way with exemptions of bus, while during the closing condition could be interpreted as evolving condition throughout the time • By using 𝛼 𝛽 indicates ratio of PPM and PPK without knowing the value of both which believed to be appropriate as variable to conduct sensitivity testing to calibrate the model Comparison of Traffic Flow and Travel Time Between Forecast and Observation Data The first step of research methodology will focused on the route choice at before closing condition with available SATURN model. Subsequently, will be compared with the real condition of route choice which acquire from link flows and travel time data to find a better value of ratio 𝛼 𝛽 . There are possibilities of step repetition as we would calculate the correct ratio for SATURN model. Second step similar with the first step however, the SATURN model is completely new with slight modifications made at the network. Consequently, the last step of research is to compare the route choice in both condition and made analysis on the main findings There is possibility of data collection to support hypothesis in findings Secondary Data Step 1 York City Network Before the Trial O-D Matrix of York City SATURN Software Forecast of Traffic Flow Each Link Step 2 York City Network After the Trial O-D Matrix of York City SATURN Software Forecast of Traffic Flow Each Link Comparison of Traffic Flow and Travel Time Between Forecast and Observation Data Main Findings Comparison of Route Choice Between Forecast and Observation Data
- 38. DISSERTATION POSTER TopicImplementation of National Urban Transport Policy of India 2006: Progress and Prospects in improving Public Transport, By: Paulose N Kuriakose 200745484Progress and Prospects in improving Public Transport, By: Paulose N Kuriakose 200745484
- 39. Ex-post versus Ex-anteAppraisal of High Speed Train Projects: Case Study on Ex-post Analysis of Turkey HST Projects Seher Demirel Kutukcu, (MA) Transport Economics Supervisor: Dr James Laird Feasility studies of the projects Passenger numbers, fares, time savings Operation and maintanence costs Before and after mode shares Load factors, delays in service (Reliability) Realized construction costs Conventional Appraisal Topics: WebTag Guidelines and Worldbank (Elasticity of demand for Ankara-Konya: literature) ( Value of time for both projects: literature ) Wider Social Impacts: TAG unit A2-1 and NCHRP spreadsheet tool Impacts on GDP: Literature Answering following questions: Do HSR projects in Turkey provide social benefits? Does it worth working on wider impacts considering their complexity? Are there good applications in the world? Is there any optimism bias in the case study Cost Benefit Analyses (CBA) and what are the reasons for deviations? Have case study project objectives been achieved? If not why not? How can ex post cost benefit analyses (CBA) contribute to the practice of ex ante cost-benefit analysis? 2. Background 1. Motivation Huge investments to HSR projects Need for prioritization of projects Problems in monetizing all benefits and costs Increasing investments in railway sector in Turkey No ex-post transport infrastructure appraisal in Turkey yet Utilization of EU funds require ex-post analysis Appraisal systems: UK Transport (WebTag), European Commission (DG Regio and DG Mobility and Transport), EIB, Worldbank (for low and mid-income countries) No standard definition on HSR. Available case studies in Turkey with accessible data ( Ankara-Eskisehir and Ankara-Konya) Ankara-Eskisehir Allocations to Railways in Turkey (Million TL) Mode Shares Before and After Ankara-Eskisehir HSR Project 4. Scope and Methodology 3. Objectives Case Study HSR Lines and Connected Cities Ankara-Eshisehir Realized Monthly Passenger Numbers 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Ankara-Konya Realized Monthly Passenger Numbers 5. Data
- 40. Prepared by :Gethin Shaji Supervisor : Jeremy Shires Co-Supervisor: Prof. Mark Wardman BACKGROUND • Desirable change in patronage can be achieved through bus design changes. • Like soft factors, impact of bus design on patronage is hard to measure and quantify. • Route 72 bus runs along Leeds-Bradford corridor. • Other bus services along this corridor are: (1) X6 (2) X11 (3) X14/14 (4) 508 • The Leeds City Region (LCR) generates 140,000 road trips daily. • The modal split along this corridor is as follows: Car – 68% Bus – 19% Train – 3% • In Oct. 2012, Hyperlink bus service was introduced along this corridor. • Key features of the Hyperlink buses are: (1) Tram like design (2) Modern leather seats with new seating layout (3) Free Wi-Fi facility (4) On-board Real Time Information (5) On-board host based ticketing ROUTE MAP OBJECTIVES • Examine and analyse ticket sales data to observe any quantifiable relation with bus design • Estimate the impact of bus design on (1) Existing bus services (2) Generational effect (3) Passenger behaviour • Observe relation between bus design improvement and change in demand METHODOLOGY DATA COLLECTION • Time period : 2011-2014 • The following buses are considered as they share part of the Leeds-Bradford corridor: (1) H72 (2) X6 (3) X11 (4) X14/14 (5) 508 • The following data is to be collected: (1) Changes in endogenous variables , e.g. fares, frequencies, journey times etc. (2) Changes in exogenous variables, e.g. the economy etc. (3) Passenger behaviour data from surveys ISSUES AND EXPECTED FINDINGS 1 • Historical analysis of bus routes from 2011 along the Route 72 corridor 2 • Analysis of ticket sales data for the buses competing along this corridor before and after the introduction of Hyperlink service 3 • Identify different demand impacts : (1) General trends (2) Abstraction from other bus services (3) Generation, e.g. new and existing bus users 4 • Analyse existing survey data and conduct new passenger survey to help understand these trends and passenger behaviour 5 • Attribute changes in demand to changes in bus design • Issues : (1) How comprehensive is the ticket data ? (2) How stable have the bus services/routes been since 2011 ? (3) How detailed are existing passenger surveys ? • Expected findings : (1) Small rise in patronage through generation (2) Small to medium rise in patronage through abstraction BUS DESIGN AND IMPACT ON DEMAND
- 41. Background Objectives Methodology • Identify currentmodelling/microsimulation approaches to shared space particularly interactions between motor vehicles and pedestrians • Assess a shared space option for the Shipley junction and improve the parameters in the current vehicle and pedestrian microsimulation model with the help of collected data to test, verify and validate the model • Assess the emission levels of the Shipley junction using the emissions model, and compare this with the signalised option for the Shipley junction • Develop general guidance that can be provided for modelling Shared Space By Samuel Oswald, cn10so@leeds.ac.uk Current Model References No Road Markings Emissions modelling The emissions model being used PHEM is a comprehensive power instantaneous model with can simulate fuel consumption and various emissions such as NOx, Particulate Mass (PM10), Carbon monoxide etc. for cars and light vehicles second-by-second. The model requires 1Hz speed data, road gradient and the vehicle specification. In this case the speed data and vehicle specifications will be taken form the AIMSUN model and road gradient form Google Earth terrain data. The AIMSUN model will also be used to predict the proportion of emissions contributed by each vehicle type (Tate, 2013). Hans Monderman first pioneered the concept of shared space, whereby removing traffic lights, signs, crossings, road markings and even curbs. Pedestrians, motorists and cyclists are required to negotiate their way through streets by reacting with one another (Projects for Public Spaces, 2002). These types of schemes have been implemented worldwide, with schemes becoming increasingly popular in the UK. In 2011 the Department for Transport issued a Local Transport Note to aid with the design of the these types of schemes however the guidance contains minimal advice on microsimulation/modelling. Yet clients are increasingly asking for models to prove designs work. There has also been relatively little research into whether this type of scheme has different affects on emissions compared to other junctions types. No Traffic SignsNo Raised Curbs AN INVESTIGATIONINTO THE CURRENT TECHNIQUES USED TO MICROSIMULATESHARED SPACES AND THE IMPACT OF SHARED SPACES ON EMISSION LEVELS Resize current model to the corridor required Validate the resized model by comparing GEH Calibration data from old model Ensure GEH analysis meets Department for Transport guidelines Assess the vehicle trajectories and dynamics in the model Compare the two sets of data Adjust the model parameters to mimic the real life data Collectreallife trajectoryand dynamicsdata usingvehicle tracking Clear model of all curbs road markings and crossings Evaluate how these vehicle movements can be applied to the junction layout Study current shared space to ascertain vehicle movements Adjust the model to represent shared space Calibrating the Base model Vehicle Dynamics Run AIMSUN with 0.5 time step without LEGION Run PHEM model for shared space model and signalised base model Run AIMSUN with 0.6 step with LEGION Google Earth Terrain data Interpolate data so it is in 1Hz form for PHEM Coding the Shared space option Figure 3: Time series plots of PHEM result for Euro 5 Bendy-Bus Projects for Public space (2002) Hans Monderman, Available online: http://www.pps.org/reference/hans-monderman/ J. Tate (2013) Project Report: York Low Emission Zone Feasibility Study – Vehicle Emission Modelling Figure 2: Example of Shared space Figure 1: Shipley AIMSUN with LEGION Base model
- 42. Price elasticity ofdemand in suburban railways in India – A case study of four cities Syed Abdul Rahman, (MA) Transport Economics Supervisor: Dr Narasimha Balijepalli • Importance of Suburban railways in India in passenger transportation. • Significance of passenger demand forecasting in transport planning, service provision and infrastructure development • Key role of elasticity in forecasting demand • Lack of economic rationale in policy decisions Complete government ownership High population growth High growth in car-ownership Fiscal pressure on Governments Need to attract private investments MRTS programs in the major cities Dynamic pricing in Indian railways 4.Scope • Four biggest cities of India: Mumbai, Chennai, Delhi and Kolkata • Time series data of 25 years 5. Data • Panel data (4 cities, 25 years) • Originating passengers, vehicle KM and fare per passenger per KM from 1988 to 2012 • Data on population, income and fuel costs 6.Methodology Concept and different methods of elasticity Decide on demand estimation models Time series regression on each individual city Linear regression on 4 cities using panel data Comparison of individual estimates with combined estimates and other studies Evaluation of available forecasts of a city and assessment of impacts on policies regarding infrastructure development 7. Risks a) Omitted variables b) Lack of studies on developing countries c) Income Vs suburban rail journey 8.References Balcombe R. et al., (2004), The Demand for Public Transport: A practical Guide, TRL Report, TRL593 3.Objectives/Output To estimate fare elasticity of suburban rail passengers To compare the estimated elasticity with other studies. To suggest possible uses of elasticity values (i.e. demand forecasting) To critically assess perspective plan of one city To highlight possible policy implications To provide input to infrastructure planning Demand model: α = constant; β = fare elasticity; γ = elasticity of population, income and fuel costs; δ = sensitivity of current demand with respect to demand in t-1 V = passenger km (dependent variable) F = Fare Z = Vector of population, income and fuel costs of the cities i = city; and t = time 1. Motivation 2.Background Mumbai Chennai
- 43. • The constructionof roads could be traced to 312 BC when Romans built roads to connect major cities within their empire. These roads were regularly maintained. • Pavement rehabilitation and construction are very expensive and do not create room for trials. • Pavement evaluation provides information to ascertain the type of maintenance activity to be carried out. An Examination and Application of Flexible Road Pavement Evaluation Methods: A review of the methods and application to roads in Sierra Leone 1. Background 2. Objectives • To identify relationships between the evaluation methods of flexible pavement. • To locate and determine the seriousness of the pavement distresses on the selected roads. • To determine possible structural surveys and/or laboratory tests to be carried out. • To ascertain the ‘present serviceability rating (PSR)’ and the ‘pavement condition score (PCS)’ for the selected roads. Transverse Crack Longitudinal Crack RuttingPatches Alligator Cracking Pothole 7. Methodology Pavement Distresses on roads • The present serviceability index would give an indication of the ride quality of the road. • The present condition score would indicate the extent and severity of the distresses on the road. 4. Why determine ‘PSR’ and ‘PCS’ ? 5. Selected Roads By Serrie Henry Willoughby MSc(Eng) Transport Planning and Engineering Mr. David Rockliff (Supervisor), Prof. Anthony Whiteing (2nd Reader ) • A comparison between the UK methods and that specified in the oversees ‘Road Note 18’ for tropical countries would be considered in this work. • The PSR would be obtained from highways engineers, commercial and private drivers. 3. Scope The relationship between the different types of evaluation method shall be obtained by review of literature. The positions of distresses shall be obtained by the use of a hand held GPS device and wheel meter. The pavement serviceability index shall be assessed by driving through the road and rating the ride quality on a scale of 0 – 5: were ‘0’ would indicate very poor and ‘5’ would mean very good. The pavement condition rating would be obtained by noting the extent and severity of distresses and calculating the index using the equations in the FHWD manual as outlined below: PCR = 100 - [(100 - AC_INDEX) + (100 - LC_INDEX) + (100 - TC_INDEX) + (100 -PATCH_INDEX) + (100 - RUT_INDEX)] • Scale: POOR (<=60), FAIR (61 - 84), GOOD (85 - 94) , EXCELLENT (95 - 100 The selected roads are Old railway line , Pademba road and Benjamin lane in Freetown, Sierra Leone. Original Pavement Design and Construction Records Analysis Establish Probable Causes of Pavement DistressesObserved Pavement Distresses Field Tests Surface Condition Roughness Deflection Frictional Resistance Field Samples Laboratory Evaluation Rehabilitation Alternatives Based on Pavement Design Principles Background Maintenance Performance Geometrics Environment Traffic Economics Thin/ Thick Overlay Inlay Recycling Reconstruction Pavement Evaluation PerformanceCondition Safety Structural Material 6. Evaluation for Rehabilitation Location Referencing
- 44. The hypotheses Open access operators exhibit the same cost characteris1cs as franchised operators Open access operators have lower input costs than franchised operators Open access operators have lower staff costs than franchised operators Scale and density effects dominates lower input costs The background Data Open access Franchises Scale effects? Density effects Other efficiency gains? Lower input costs? The objec?ve The paper will seek to answer two major ques1ons: • Does open access operators face a different cost func?on from that of franchised operators? and • Does other efficiency gains and/or lower input costs outweigh the scale and density effects? The separa1on of rail infrastructure and train opera1ons, is making the passenger rail market less monopolis1c. Governments can open for either compe11on-‐for-‐the-‐market or compe11on-‐in-‐the-‐market. The former is through franchise or tender compe11ons for a train opera1on monopoly. In the laFer model, companies run services on their own risk and in compe11on with each other. Research on franchised passenger rail opera1ons done in the UK by Wheat and Smith (2014) has shown that there are strong economies of density (number of trains run per kilometre of track) and in some cases significant scale effects (route network size) in the industry. However, this research did not account for open access opera1ons. With a growing market for such opera1ons in Europe, it is important to establish whether open access opera1ons exhibit different characteris1cs from franchised operators. The answer may provide a guide to whether head on open access compe11on will bring benefits, or if we are beFer off following the UK prac1ce of only allowing non-‐abstrac1ve services. United Kingdom – non-‐abstrac1ve services since 2000 Sweden – head on compe11on since 2010, major increase expected in 2015 Czech Republic – head on compe11on since 2011 Interna1onal open access opera1ons liberalised in 2010 Austria – head on compe11on since 2011 Italy – head on compe11on since 2012 Germany – head on compe11on since 2012 Dates determined by start of first services as recorded by Railway GazeFe Interna1onal online. Flags sourced from na1onalflaggen.de Current open access opera?ons Econometric analysis of open access railway operators’ compe??veness Data • Collect and structure data for use in model. • Need to be to the same format as data used in earlier research to ensure comparability Model • Using hedonis9c cost model for heterogeneous outputs developed by Wheat and Smith (2014) • This will provide me with a cost func9on for open access operators Tes?ng • Is the cost func9on for open access operators different from that of comparable franchised operators? • If using the input/staff costs of franchises, would the costs of open access increase? • Does the lower input costs make up for the change in scale and density effects? The Methodology Due to open access operators only being responsible for a minor frac1on of the traffic on the Bri1sh network, there is a risk that size and form of the sample will limit the transferability of the results. However, the results should indicate whether there are structural cost advantages to be exploited, as well as formalising the cost structure of small rail operators in the United Kingdom. Risks By Tørris Rasmussen Supervisor: Phil Wheat Output metrics such as train hours, train kilometres and route kilometres, and input metrics such as vehicles per train, vehicle type and top speed will be sourced from 1metable data from the Department for Transport. The minimum require data has been secured. Cost input data will be sourced from respec1ve companies’ annual accounts posted with Companies House and publicly available. Alexandersson, G., 2010. The Accidental Deregula9on -‐ Essays on Reforms in Swedish Bus and Rail Industries 1979-‐2009. Ph.D thesis: University of Stockholm. Wheat, P. & Smith, A., 2014 Forthcoming. Do the usual results of railway returns to scale and density hold in the case of heterogeneity in outputs: A hedonic cost func9on approach. Journal of Transport Economics and Policy MVA Consultancy, 2011. Modelling the Impacts of Increased On-‐Rail Compe99on Through Open Access Opera9on, London: MVA Consultancy. Major sources consulted
- 45. Sustainable Aviation: Evolution ofChina’s Aviation Industry (Markets and Fleet) STUDENT: Wendy Dzifa Wemakor SUPERVISORS: Professor Andrew L. Heyes & Dr Zia Wadud Introduction Currently, China’s domestic fleet is made up of two aircraft types , turboprops and regional jets. There are a total of 725 flight routes of which 655 flights are served by regional jets and 70 by turboprops (Ryerson and Ge,2014). As cities and personal wealth increases, so will the intensity of human interactions with the result that there will be and increase in demand for air travel. The aircraft will have to grow and evolve to meet the need. Objectives China’s aviation industry is a significant contributor to its economy. In 2010, the industry contributed about 1% of total GDP and 296 million passengers and 11 million metric tons of freight travelled to, within and from China (IATA,2012).Tremendous growth in China’s aviation industry is expected by 2020 and will take the highest value of aircraft deliveries in the period as predicted by Boeing and Airbus, the world’s largest manufacturers of aircrafts. To develop a representative model of the development of air travel in China in order to • assess the world fraction of China’s aviation market and how it will change over the next two decades. • determine the optimum aircraft fleet to meet China’s air travel demand in the most energy efficient way. • compare air travel between China’s cities with the High Speed Rail from an energy perspective The demand Dij between cities i and j is calculated using a simple one-equation gravity model of the form 𝐷𝑒𝑚𝑎𝑛𝑑 𝐷𝑖𝑗 = 𝐾 ∗ 𝑝𝑜𝑝𝑖 𝑝𝑜𝑝𝑗 𝛼 ∗ 𝐺𝐷𝑃𝑖 𝐺𝐷𝑃𝑗 𝛽 ∗ 𝑐𝑜𝑠𝑡𝑖𝑗 𝛾 The forecast will help determine fleet type to meet demand from an energy perspective. Methodology Analysis of Air Travel Demand Model Optimization of Fleet Type Comparison of Results Air Travel and HSR Discussion s and Conclusion Gravity Demand Model Optimization of Fleet to Meet China’s Demand Expected Output Regional Jet / HSR/ Turboprop? It is hoped the study will allow conclude whether or not aviation is the optimum way to link China’s vast growing cities and how it can be integrated with other transport modes like the high speed rail to form a sustainable transport system Whatwillitbe? 2013/14 Figure 1: Aircraft demand in global aviation market (Airbus, 2012) Reference • Airbus. 2012. Global Market Forecast 2012-2031. • International Air Transport Association. 2012. Special report: Chinese aviation. A New Era in Aviation. • Ryerson, M. S. and Ge, X. 2014. The role of turboprops in China’s growing aviation system. Journal of Transport Geography.
- 46. How Advanced TicketPurchase Influences Yield Management In Rail Market? Yield management originated with the deregulation of the US airline industry in the late 1970s. Railroads have offered a limited number of tickets online at a discounted price in advance. •The PEP system offered base fares as well as early booking discounts of 40%, 25%, or 10%. BACKGROUND To investigate yield management in rail market; To investigate what impact risk has on the decision whether to purchase a ticket in advance or not; To help understand how to maximise the revenue of rail companies and how this conflicts with the benefits for passengers; To calibrate the existing PRAISE model in modelling advanced ticket purchase to be more realistic. OBJECTIVES METHODOLOGY Analyse the Value of Advanced Ticket Purchase Analyse the disadvantages Reveal economic benefits Collect the ticket prices online for One and Two months in advance Existing PRAISE Model: Demand Model Structure 𝑷 𝒕𝒊𝒄𝒌𝒆𝒕 = 𝑷′ 𝒓𝒂𝒊𝒍 𝑷 𝒕𝒊𝒄𝒌𝒆𝒕|𝒓𝒂𝒊𝒍 Multinomial Logit Model 𝑃𝑡𝑖𝑐𝑘𝑒𝑡|𝑟𝑎𝑖𝑙 = exp( 𝑼 𝒕𝒊𝒄𝒌𝒆𝒕) exp( 𝑈 𝑡𝑖𝑐𝑘𝑒𝑡′)𝑡𝑖𝑐𝑘𝑒𝑡′∈𝑁 Incremental Logit model 𝑃′ 𝑟𝑎𝑖𝑙= 𝑃 𝑟𝑎𝑖𝑙exp(∆𝑈 𝑟𝑎𝑖𝑙) 1−𝑃 𝑟𝑎𝑖𝑙 +𝑃 𝑟𝑎𝑖𝑙exp(∆𝑈 𝑟𝑎𝑖𝑙) Upper Level Lower Level Fare Function 𝑵𝒆𝒘 𝑼 𝒅𝒂𝒕𝒆 𝑮𝑪 𝒅𝒂𝒕𝒆= 𝑭 𝒅𝒂𝒕𝒆 + 𝒗𝒐𝒕 ∗ 𝑮𝑱𝑻 𝒅𝒂𝒕𝒆 + 𝑨𝑷 𝑨𝑷—Advanced Purchase Penalty 𝑭 𝒅𝒂𝒕𝒆—Fare Function deduced by data A Route on High Speed Rail Network in an European Country Data Collection Calibrate PRAISE model 𝑷 𝒅𝒂𝒕𝒆 = 𝑷′ 𝒓𝒂𝒊𝒍 𝑷 𝒅𝒂𝒕𝒆|𝒓𝒂𝒊𝒍 CASE STUDY • Devise Scenarios involving different levels of fare adjustment to analyse different yield management systems; • Calculate the Revenues and Demands for different Scenarios; • Compare with the Base Scenario (current fares) to put forward Price Suggestions. Xinghua Zhang -MSc (Eng) Transport Planning and Engineering Supervisor : Daniel Johnson E-mail: ml12xz@ leeds.ac.uk Institute for Transport Studies University of Leeds 05 - 2014
- 47. Yodya Yola Pratiwi SpeedLimits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit Yodya Yola Pratiwi Speed Limits for UK Motorways: a case study of increase the speed limit
- 48. Electric Bikes: asolution to hills? Lessons from case of China . In the chart, the growth rate of E-bikes was predicted to show a sharply increase in Western Europe while the others remain stable from 2013 to 2020. Easily found in the terrain map of UK, the most land of UK is covered by rugged hills and low mountains, especially from the middle to the north. Find out the potential users of E-bikes in UK, range from different age and gender. In order to improve the market of E-bikes in UK, the appropriate price and what will the consumers consider when deciding to buy a E-bike should be identified. The usage of E-bikes. UK China Zhixi Li – ts13zl@leeds.ac.uk Msc Transport Planning Supervisor: Frances Hodgson ITSUniversity of Leeds Background1 Objectives2 Methodology3 Study of UK Questionnaire Classification of Respondents 1 Gender 2 Age Case Study of China to inform UK Focus Group Literature Review 1 Government publication 2 News Report Potential outcomes5 Scope4 UK Age Gender Standard 20-40 Male • 60 people will be surveyed for each age group, and 30 for each gender. • Online surveys and practical surveys will be done on the same time for all the age group Female 40-60 Male Female 60-80 Male Female China (second tier city) Drivers Pedestrians and cyclists China began to produce E-bikes since 1998 and have the most amount of E-bikes users, increased from several thousands to more than 10 million in 2005. Lacking of appropriate improvement strategies and regulations leads to a lot of traffic congestions and accidents. Introduction E-bike is a kind of tool which take hills out of riding (Goodman, 2010). UK government defined Electric bikes as “electrically assisted pedal cycles”(EAPCs) which should be 2- wheeled bicycles, tandems or tricycles with a battery pack and a motor to transfer power. The definition of E-bikes in China has a greater range, included E-bikes, E-scooters, Mobility Scooters. Background of theses two countries The research will seek to identify the advantages and disadvantages resulted from using E-bikes to the citizens of China, while the reason why E-bikes are so popular in China will be found. By review the documents related to E- bikes in china, find out the potential problems to the whole transport system, and make suggestion for an appropriate improvement strategies and policies. Most of the young people might use E- bikes for leisure, therefore E-bikes may be more popular for elder people, the survey will help to find the reason of it Price and safety reasons may be the most important thing to concern After the case study of China, Appropriate policies and improvement strategies should be determinate and published, in order to make suggestions for UK Infrastructure and related facilities would be well designed and built up before E- bikes plan being practiced
- 49. DOES PAINTING THETOWN RED WORK? The study area will be the city of Leeds, which means that the cycling lane sections that can be potentially analysed will lay inside Leeds boundaries and the proposed questionnaires can only be answered by Leeds residents or people from other cities who perform any kinds of activities here. As we cannot make observations in all cycle lane sections of the city, we have set the criteria for the selection, prevailing those locations where lane violation is more probable, as junctions, high dense traffic roads or areas where economic activities attract an important number of vehicles, overloading parking facilities. AN APPROACH TO THE LEEDS CITY CASE City Councils all over the world paint their cycle lanes in vivid colours, following the belief that this is the best option to make them visible for other road users and to provide a feeling of safety to cyclists. But…is it actually useful? What are the implications of this measure? Are there any alternatives? We will try to give an answer studying Leeds road users’ behaviours and attitudes. road safety? cycle lanes levels of use? local budgets? METHODOLOGY AND DATA COLLECTION We will carry out observations at some selected points in order to collect data about cycle lane violations in painted and non-painted sections and which reasons cause them, but it is also intended to study cyclists’ behaviour (e.g. if they look comfortable in these lanes or if they do not used them and prefer general circulation lanes). Previously, we need to design a predefined form to gather the same kind of information at all the observations It is the first stage of the investigation. We are checking out if some kind of research related to this topic has been carried out in Leeds or in other cities, as it will help us to set the methodology and make comparisons between our further results and those existing ones. We will look for general arguments in favour and against painting cycle lanes from different points of view: (technical, economical and psychological). Furthermore, alternatives to painting cycle lanes will be explored, in order to make if those experiences have been successful and can be transferable to Leeds roads We want to know if painting the cycling lanes makes them actually safer, that is, the probability of a lane violation, and thus, a possible crash between a cyclist and other road user are smaller when the cycling lane is painted. Are there any technical problems derived from a painted surface? We make ourselves this question as we want to discover if the used paint matches the safety standards to avoid accidents when weather conditions are not favourable. Do painted lanes encourage people to use them? Is it because they perceive them as safer, more attractive, or as an effective tool to reduce their travel times/budgets? Are there other factors that affect cycle lane usage apart from the paint? Other aspects as complete segregation from general circulation or shared lanes with buses can be also decisive. Are there effective alternative measures/devices with the same visual effect of paint? We want to explore other beneficial alternatives for all road users. Painting and maintaining cycling lanes has a price. And we want to know if Leeds City Council would be able to save a significant amount of money running a different painting-lane policy. But what about the price of alternative measures? By knowing it, we could make a trade-off between them and the benefits they can potentially provide. David Rodriguez Martin Year 2013/2014 MSc Transport Planning Dissertation WHATARETHE BIKE LANE Questionnaires are also a key part of the data collection, because we are using them to reach a better understanding of opinions, behaviours and perceptions. We need data from all road users (cyclists, motorcyclists, car drivers and pedestrians) as they will have different views on the topic. Different questionnaires for every road user group have been designed and sent to people. This stage will not finish until mid June, as the more people decide to participate, the bigger will be our sample and then more representative our results. It is also important to codify all possible answers to make the analysis stage easier. SCOPEOF IMPACTSON… THESTUDY Questionnaires Direct observation Literature review BIKE LANE All the data collected will be analysed and the results will be expressed through texts, charts and tables. Statistical operations will be performed also at this stage. Then, having understood all the data collected and, taking the previous literature into account, we will reach to a conclusion, trying to answer the research questions. Analysis of results and conclusions UNIVERSITY OF LEEDS Source: www.ecomovilidad.net Source: www.zicla.com
- 50. ECONOMY ENVIRONMENT + Reducingthe cost of import fossil fuels + Gaining profit by export biofuels + Reuse abandoned agricultural land − Low energy density of biofuels, lead to more distances, travel times and labour costs. − Feedstock processing costs + Less GHG emission + Carbon negative renewable energy − Extra trips/distances are needed for collecting feedstock cause greater air pollution − The changes of Land use cause emissions − Decrease biodiversity − Decrease soil, water quality SOCIETY ENERGY SECURITY + Increase employments + The fuel prices become more reasonable − Food security concerns − Poor quality of biofuels decrease the vehicles performances − The hi-tech vehicles are not affordable for the whole public, increase the inequality. + As an alternative energy source to enhance energy security − The failure of implement biofuels can weaken energy security − Hard to forecast if biofuels face the same situation like oil crisis in the future “4ASPECTS”OFIMPACTSAIMS&OBJECTIVESMETHODOLOGY Rose I-Hsuan Lien (ts13ihl@leeds.ac.uk), Supervised by Dr Anthony Whiteing iofuel’s issue used to focus on its energy efficiency and the food crisis it might cause. However, how to optimise the supply chain still lack of discussion at present. To illustrate, negative impacts of biofuel supply chain not only need to be recognised but be solved through sustainable ways. The main missions of this supply chain management are to ensure the costs are competitive and the supplies are continuous (Gold and Seuring, 2011; Hess et al., 2007; Sims and Venturi, 2004). The demands of biofuel in Taiwan are increasing. Since the usages of biofuels in Taiwan are still developing, it is an advantage for Taiwan to implement the sustainable strategies into the supply chain. Harvest & collect ·Feedstock type ·Harvest frequency ·Topography ·Post-harvest process place (bail and chip) Storage ·Demands & supplies ·Storage facilities ·At farm or storage terminal ·Degradation & dry matter loss Pre-treatment · Pre-treatment with storage · Mode: drying, pelletisation Energy conversion · Place for refine and blend Gas station · The facilities to store biofuels Consumer · Vehicle performance · Vehicle choices Transport:Modechoice,law&infrastructure,vehiclescapacity Reference: ①Green Energy Industry Information Net, (2014). Introduction of biofuels Industry. Taiwangreenenergy.org.tw. Available at: http://www.taiwangreenenergy.org.tw/Domain/domain-4.aspx ②Hess, J.R., Wright, C.T., Kenney, K.L., 2007. Cellulosic biomass feedstocks and logistics for ethanol production. Biofuels, Bioproducts and Biorefining 1 (3), 181e190. ③Gold, S. and Seuring, S. 2011. Supply chain and logistics issues of bio-energy production. Journal of Cleaner Production, 19 (1), pp. 32--42. ④IEA. 2013. Tracking Clean Energy Progress 2013. [e-book] Paris: Internation Energy Agency. Available through: Internation Energy Agency http://www.iea.org/publications/TCEP_web.pdf. ⑤Sims, R.E.H., Venturi, P., 2004. All-year-round harvesting of short rotation coppice eucalyptus compared with the delivered costs of biomass from more conven- tional short season, harvesting systems. Biomass and Bioenergy 26 (1), 27e37. B 1. Case studies: Studying biofuel promoting schemes in developed/emerging/developing countries to compare and organise the implementations under different circumstances. 2. Interviews: To apply potential options to Taiwan, the locals’ opinions and experiences are important. Thus the options will be provided to experts in different areas - academic, planner, government, sustainable workers in Taiwan, and interview them to know the attitudes and possibilities to implement these options. PROBLEM → APPROACH → SOLUTION → ADAPTION → APPLICATION Understand biofuels implementation Understand operation processes Develop sustainable Instruments Develop options for Taiwan Summarise the feasibilities of options ①Policies& infrastructures ②Impacts in “4 aspects” ③Future goals & trends ①”Who” drive &“who” are involved ② Operation processes ③Differences between nations ①Instruments integration ②Factors of instrument choices ③Underlying barriers and conflicts ④Strengths & weaknesses in “4 aspects” ①Develop options ②Stakeholders’ viewpoints ①The most and the least feasible options ②Influents of choices ③Conflicts of influents between different stakeholders ④Improve options: negative↓, positive↑ (Gold and Seuring, 2011) Case in Taiwan: Bioethanol: (Import only, no local factories (high investment risk)) •07/2009:Taipei&Kaohsiung Metro Area Ethanol Promotion Project, total 14 gas stations supply E3. •2012: 210 kilolitres used. Biodiesel: (Imported palm oils and local cooking waste oils) 4 stages implement plan: 1.11/2006-06/2008: Green Bus Project, encouraged buses use B1-B5 biodiesel 2.07/2007-06/2008: Green County Project (B1), in two counties, the oil companies in these areas were chosen to blend biodiesels and to sell in the gas station there. B100 biodiesel were refined by Taiwan factories , also energy crops were allocated to plant in fallow farmlands. 3.07/2008: Full implement B1 4.06/2010: Full implement B2 • 2012: 100,000 kilolitres used. • 2014: Poor quality of biodiesels influenced vehicles performance, government plan to pause B2 projects in June. • 2016: Full implement B5, estimated 250,000 kilolitres biodiesel required. (Green Energy Industry Information Net, 2014) OPERATION ISSUES Global biofuel productions projections and target. (Source: IEA, 2013)
- 51. Analysing of theRelationship Between Accessibility and Customer Satisfaction for the Evaluation of Transport Plans Ioanna Moscholidou, MSc Sustainability Supervisor: Astrid GühnemannTHE CASE OF WEST YORKSHIRE Evaluation plays a key role in transport planning as it allows the timely identification of strengths and weaknesses and the readjustment of policies and measures (Burggraf and Gühnemann, 2014). The evaluation of accessibility is an area that attracts increasing attention in transport planning. However the challenge of identifying the correct measures for each situation remains. It is suggested that for meaningful assessment accessibility levels should be disaggregated for different modes and social groups and linked to other indicators to allow contextualisation (Geurs and van Wee, 2004). In existing research the links between the public transport supply and the perceived service quality have not been clearly established. It is suggested that the subjective views of customers are highly context-dependent and not only do they reflect what they get but also how they get it and who they are (Friman and Fellesson, 2009). The theoretical background The methodology The objectives Source: Metro, 2014 Source: Vector Research, 2012 6.9 satisfaction with rail services 7.2 satisfaction with bus services 67% of the population has access to workplace within 30 minutes using public transport The 2011 baseline Access to employment % of working population able to access key employment centres within 30 minutes using the core public transport network. Satisfaction with transport The indicator combines satisfaction scores across modes and assets. Scored out of 10. The Local Transport Plan Targets 75% 67% 7.0+ 6.6 The West Yorkshire context Metro, the West Yorkshire passenger transport executive, implemented its 3rd Local Transport Plan (LTP) in 2011. The plan covers the period until 2026 and the first phase of evaluation ended in April 2014. Over the first three years the transport authority faces the challenge of maintaining the high quality of services despite the public spending cuts. List of references: Burggraf, K. and Gühnemann, A. 2014. Why is monitoring and evaluation a challenge in sustainable urban mobility planning? Report for the CH4LLENGE Project. Available from: http://www.sump-challenges.eu/content/monitoring-and- evaluation (last accessed 25/04/2014) Friman, M. and Fellesson, M. 2009. Service supply and customer satisfaction in public transportation: The quality paradox. Journal of Public Transportation. 12(4), 57-69. Geurs K.T. and Bert van Wee, B. 2004. Accessibility evaluation of land-use and transport strategies: review and research directions. Journal of Transport Geography. 12, 127–140. Vector Research. 2013. Final Report- Tracker Survey 2011 for Metro. 1. To evaluate their performance of LTP3 in terms of accessibility and customer satisfaction. 2. To identify any correlation between the accessibility and satisfaction and provide an explanation for the underlying factors of the result. 3. To provide an insight on how the ex-post evaluation results can contribute to the improvement of ex-ante appraisal. 1. Four accessibility and four customer satisfaction indicators will be evaluated against the targets set by the LTP in a checklist format for a 3-year period (2011-2013). The analysis will be done using ACCESSION and ArcGIS. 2. The accessibility indicators will be correlated with overall satisfaction and a corresponding satisfaction indicator using spatial regression and hypothesis testing. The analysis will be done using the R software. Hypothesis: Accessibility and satisfaction are not correlated. 3. The correlation results will be analysed using contextual data from 2011 Census and Metro surveys in order to provide a clearer view of the West Yorkshire background. Accessibility (data from Metro) • % of working population with access employment within 30 minutes using public transport • % of population within a zone of 200m around public transport stops/stations • % of population with access the urban centres within 30 minutes using public transport • % of population within a zone of 200m around public transport accessible stops/stations Customer Satisfaction (data from Passenger Focus) • Overall satisfaction • Satisfaction with distance of the stop/station from the journey start • Satisfaction with convenience/accessibility of stop/station location • Satisfaction with on-vehicle journey time The indicators 0 10 UNIVERSITY OF LEEDS Institute for Transport Studies
- 52. Playing with transport Howto make your commute greener and have fun at the same time. Objectives Methodology This dissertation will try to understand if there is a difference in mode choice when people are given incentives to use modes of transport that are friendlier with the environment. The incentives investigated will be based on gamification where an aim is set and participants compete to win by making decisions that impact their behaviour. Data analysis and expected findings A 2 week field study will be carried out to assess the impact that providing incentives has on commuting behaviour. Participants will be asked to install an app on their phones and use it for 2 weeks. During the first week, the app will record information on participants’ trips, including distance travelled, mode choice, route choice (coordinates) and duration. During the second week, participants will receive points based on their use of different modes. Information on their ranking amongst the group will be disclosed. Points will be converted into raffle tickets for a chance to win prizes. Background With urban pollution on the rise, policies that incentivise modal switch towards modes of transport that are better for the environment are needed. Gamification, or the art of mixing games and real life, has shown to be an effective way of modifying behaviour to achieve a specific goal(1); whether it is user engagement, learning, or even physical exercise. Gamification can be used as a way to reward positive behaviour rather than punish negative behaviour, and as such may be well perceived by the public and prove more effective than punitive policies such as congestion charging. J. SEBASTIAN CASTELLANOS Supervisor: Frances Hodgson Bibliography and further reading 1. Muntean, C. I., 2011, Raising engagement in e-learning through gamification, 6th International Conference on Virtual Learning. 2. McCallum, S., 2012, Gamification and serious games for personalized health, 9th International Conference on Wearable Micro and Nano Technologies for Personalized Health. 3. Brazil, W. and Caulfield, B., 2013, Does green make a difference: The potential role of smartphone technology in transport behaviour, Transportation Research Part C: Emerging Technologies, Vol. 37, pp. 93–101. 4. Dick, E., Knockaert, J., and Verhoef, E., 2010, Using incentives as traffic management tool: empirical results of the “peak avoidance” experiment, Transportation Letters: The International Journal of Transportation Research Vol. 2, pp 39-51. 5. Fan, Y., Chen, Q., Douma, F., Liao, C., 2012., Smartphone-Based Travel Experience Sampling and Behaviour Intervention among Young Adults, Intelligent Transport Systems Institute, University of Minnesota. 6. Schlossberg, M., Evers, C., Kato, K., and Brehm, C., 2012, Active Transportation, Citizen Engagement and Livability: Coupling Citizens and Smartphones to Make the Change, URISA Journal, Vol. 25, No. 2. The app Participants click on “start trip” whenever they want to record a trip. While travelling, participants take a picture to confirm the mode they’re using and hence become eligible for points When the trip is finished, participants click on stop and choose the mode they used. The information is sent to a server where it is collected Sample trips Output files With the data collected during the first week of the study, a baseline scenario will be set up and the modal split of the sample will be determined. During the second week, changes in modal split will be observed and a hypothesis test will be carried out with H0 being there is no change in modal split when incentives are in place, and H1 being there is a significant change in modal split, specifically towards higher rewarded modes. Field study The field study will be carried out in Bogotá (Colombia), with students between the ages of 17 and 24. The sample size will be between 30 and 40 participants. Data analysis Mode Points Car 0 Public bus 2 BRT 4 Bicycle 6 Walking 6
- 53. Noisy optimisation: Stochastic optimisationof traffic signal networks using a trust region method Jack Robinson, MSc (Eng) Transport Planning and Engineering candidate, Institute for Transport Studies, University of Leeds For a network of traffic signals with a given common cycle time, which set of green times and offsets minimises the total travel time of vehicles through the network? Problem formulationI • The problem is too difficult to solve analytically for general networks • The objective function may contain multiple local minima, making hill-climbing methods unreliable • Stochastic effects from simulation further distort the objective function Set parameters, and initial timings and trust region settings Simulate network in DRACULA for various timings within the trust region Fit model function to simulation results Find solution to minimise model function Update trust region centre and size Output solution Loopuntilsolutionconverges • A framework for derivative- free optimisation, useful for complicated surfaces • An easy-to-optimise model function is fitted to the objective function in a ‘trust region’ around the current best solution • For this project, quadratic model functions are used, and the trust regions are ballsSchematic contour plot of trust region model function True function: Model function: Trust region: Compare resilience and speed of the algorithm under different parameters, networks and starting conditions • The algorithm is being tested for a simple two-node network • Testing will continue on larger networks, and the algorithm will be refined • Is the algorithm adversely affected by the randomness of simulation? Use of the simpler, deterministic cell transmission model could be compared Photo by Edwin Mak 10 20 30 40 50 60 0 0.2 0.4 0.6 0.8 1 Total veh-hours in network Empiricalcumulativeprobabilitydistribution Empirical cumulative distribution plot of a test run of the trust region algorithm on the two-node network after 0 iterations after 1 iteration after 2 iterations after 3 iterations after 4 iterations after 6 iterations known solution Issues2 Algorithm structure3 Trust region methods4 Analysis5 Progress and plans6
- 54. Block signalling The conceptof block signalling is simple. The railway line is split into blocks of varying lengths, controlled by a signal at the entrance to each block. Once a train is within a particular block, no other train can enter the block until it is clear. This was introduced as a safety precaution in the late 1880’s and is still used in places on the network today. Moving block signalling Much like block signalling, moving block signalling uses the ideas of blocks as well. The difference is that the blocks re fixed to each individual train. The block is the length of the train, plus the distance that the train would need to stop in case of emergency. This allows trains to run a lot closer together in a safe manner. Premise A hot topic today surrounding railways is the issue of capacity. With demand for rail services increasing the railways are becoming busier. In terms of the environment there is also call for freight to be transported via railways instead of roads and so this puts further strain on the railway network. HS2 is a solution to this increase in demand but since its completion is 20 years away, there needs to be a solution now. Moving block signalling is one of those solutions and this project will look at what sort of capacity gain the railway could experience by changing its approach to signalling. Modelling Using the specifications from real locomotives that are operating on the East Coast Mainline, the length of each block can be calculated. At stage one, the new capacity can be calculated under the assumption that all trains run at the same speed. At stage two, the varying speeds of trains, freight vs passenger, can be taken into account using duel lines to facilitate an optimal solution for capacity. Outcomes and Analysis After the modelling stage is complete there will be an optimal solution in terms of the maximum capacity of railways. This will be reviewed in terms of the modal shift of freight and the environmental benefit as well as the passenger rail demand benefit. The trade offs will also be reviewed to see if there are any negative effects of moving block signalling.
- 55. A multi-national analysisof the value of travel time: the study case of UK and DK UNIVERSITY OF LEEDS Institute for Transport Studies Background and motivation The value of travel time is the key parameter in transport economics1. Its definition plays a major role in: • Investment appraisals • Travel demand forecasting The ample variety of results across models and studies is a matter of concern. In the UK, estimated values of the VTT differ up to 69% within the same data set2. • What is the nature of the multiplicity of results? • Are complex models diminishing our ability to obtain «robust» empirical evidence on the willingness to pay? • What is the role of the data set? Is model selection to blame for the differences? Evidence in Australia and NZ suggest that "as models become more complex, there is greater variability in the mean estimates of VTTS between data sets"3. Source: Hensher, et. al (2012) Leonardo Ortiz Olivares Prof. Stephane Hess (Supervisor) Objectives • To explore if significant differences exist on the estimation of the mean VTT across different choice models within the same data set for the UK and DK cases. • To provide some insight on the implications of model structure selection (contrasting models). A first look at the evidence Source: UK data from Tjiong (2013) and Mackie (2003); DK data from Fosgerau (2006) 4.22 7.13 1.03 1.49 MNL MMNL UK (p/min) DK (DDK/min) Methodology Multinomial Logit Model (MNL), Mixed MNL (MMNL) and Latent Class Model with identical set of attributes and functional form will be developed to calculate comparable VTT across models. To determine the influence of the data or functional form in the VTT estimated values, a multivariate regression will also be estimated: where i indicates the estimated model and all independent variables are specified as dummy (1,0). Data British, Danish and Dutch national VOT studies have been built on similar time-cost experiments. Access to similar data sets in design allows to contrast results within model form across comparable data sets. • Orthogonal survey designs • Two unlabelled alternatives • Non-business trips included • Similar socio-economic variables References 1FOSGERAU, M. 2006. Investigating the distribution of the value of travel time savings. Transportation Research Part B: Methodological, 40, 688-707. 2TJIONG, L. K. J. 2013. Re-estimating UK value of time using advanced models. MSc Transport Planning, University of Leeds. 3HENSHER, D. A., ROSE, J. M. & LI, Z. 2012. Does the choice model method and/or the data matter? Transportation, 39, 351-385. MACKIE, P., WARDMAN, M., FOWKES, A., WHELAN, G., NELLTHORP, J. & BATES, J. 2003. Values of travel time savings UK. 𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖 = 𝐶𝐶 + 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 + 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 + 𝐿𝐿𝐿𝐿𝑖𝑖 + 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑈𝑈𝑈𝑈 + 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷
- 56. Investigating and Calibratingthe dynamics of vehicles in Traffic Micro-simulations Models Mohammed Yazan Madi - Supervisor: Dr. James Tate University of Leeds ts13mym@leeds.ac.uk Objectives Transportation sector is a significant contributor to air quality problems in the United Kingdome . Traffic micro-simulation models are used to generate second-by- second speed trajectory information for use by instantaneous emission models to evaluate the environmental impact of real-time Background Develop a calibration process of micro simulation models to ensure vehicular behavior from simulation is closer to the behavior observed in the field, which improve second by second vehicle activity and emissions estimates. Validate micro-simulation vehicles' dynamics with field second by second trajectories data to explain any trends or unique observations in emissions The following data will be collected : Data Collection Objectives ResearchMethodology emission models to evaluate the environmental impact of real-time transport policies. Vehicle dynamics are explained by the driving velocity and acceleration / deceleration of vehicles. It is significant that the vehicle dynamics replicate on-road behavior, so the emission model’s simulations of engine power demands and resultant fuel consumption/ emissions are reliable. For environmental models to be reliable, vehicle dynamics in traffic micro-simulation models need to replicate on-road behavior. Background Case study Location Micro-simulation Model trajectories data to explain any trends or unique observations in emissions estimates from the simulation and the real-world. The motivation of this dissertation is to apply these advanced models to create a state-of-art traffic micro-simulation models that are real-world integrated and can evaluate the environmental impacts of different traffic management strategies. Research Hypothesis: Parameters of internal behavioral models within microscopic simulation packages need calibration and validation to better replicate vehicle activity on roads. ResearchMethodologysimulationModel Headingley, Leeds, UK. Micro-simulation model calibration and validation process framework : Calibration and Validation Process Approach Vehicle Movements in AIMSUN RaceLogic VBOX II GPS engine and data logger is used to obtain dynamic characteristics data of vehicle (speed, acceleration) and geographical position. AIMSUN microscopic traffic simulator will be used to model the Headingley corridor traffic in Leeds to measure second-by-second speed trajectory information. AIMSUN Specifications List your information on these lines. Micro-simulationModel Traffic-emission Modeling Framework Micro-simulation model calibration and validation process framework : AIMSUN Micro- simulation Model AIMSUN Micro-simulation Model Calibration using key input data , and simulation model parameters (observed aggregated traffic flow and average travel speed) AIMSUN Micro-simulation Model Validation using activity data AIMSUN Calibrated Model Run Demand/ Flows (DMRB procedure, GEH stat) Journey times (DMRB criteria) Vehicle Trajectories Vehicle speed and acceleration Vehicle Movements in AIMSUN ResearchModel The traffic model captures the second-by-second speed and acceleration of individual vehicles travelling in a road network based on: Vehicles individual driving style. Vehicle mechanics. Vehicles interaction with other traffic and with traffic control in the network. During simulation each vehicle is moved along their chosen route from origin to destination. Their speeds and positions are updated according to car-following, lane-changing and gap acceptance rules, and traffic regulations at intersections.. Example DataAnalysis ResearchApplication • AIMSUN real–world integrated micro- simulation model. TRAFFIC MICROSIMULATION • Instantaneous emission model PHEM 11. VEHICLE EMISSION MODEL • Road section, time-of- day, vehicle sub-category or an individual vehicle trajectory. RESULTS Vehicle Trajectory Data Disaggregate Emission Data VEHICLE TYPE (Car, LGV, HGV) VEHICLE SUB- CATEGORY (Euro, Fuel type) Frequency distribution of Observed and Modelled passenger car speed and acceleration distributions (hexagonal binning). Source (Tate, 2013) ANPR SURVEY Tate, J. 2013. York Low Emission Zone Feasibility Study- Vehicle Emission Modelling. Institute for Transport Studies, University of Leeds. Source (Tate, 2013)
- 57. The Importance ofEco-Driving In the last decades, engine technology and vehicle performance has improved, while most drivers have not adapted their driving style.1 Eco-driving represents a driving culture which suits modern engines and makes best use of advanced vehicle technologies, such as maintaining a steady speed at low rpm, shifting gear early and rolling in neutral. The benefits of adopting such behaviour include; Lower greenhouse gas emissions It is widely accepted that greenhouse gas emissions contribute to climate change. Eco-driving can be embraced as a simple and effective environmentally friendly initiative that can lower vehicle emissions by 20g per kilometre.2 Save cost on fuel By adopting eco-driving practices, fuel efficiency can be improved by 15% or more lowering the demand for fuel.3 Source (Fiat, 2010, p. 25) Eco-driving: who does it and why ? Identifying the motivational factors References 1 Eco Drive (2014). What is eco-driving. [Online]. [Accessed 7th April 2014]. Available at: http://www.ecodrive.org/en/what_is_ecodriving-/ 2 Fiat. (2010). Eco-driving Uncovered. [Online]. Accessed 7th April 2014]. Available at: http://www.fiat.co.uk/uploadedFiles/Fiatcouk/Stand_Alone_Sites/EcoDrive2010/ECO-DRIVING_UNCOVERED_full_report_2010_UK.pdf 3 Baltutis, J. (2010). Benefits of Eco-Driving. [Online]. (Accessed 7th April 2014]. Available at: http://www.unep.org/transport/PDFs/Ecodriving/Ecodriving_pwpt.pdf 4 Mensing, F et al. (2014). Eco-driving: An economic or ecologic driving style? Transportation Research Part C, 38, 110 – 121. 5 Powell, D. (2008). Extreme Driver – with a difference. New Scientist. 200, pp. 42 -43 6 DfT. (2011). Eco-driving: Factors that determine post test take up. [Online]. [Accessed 7th April 2014]. Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/142536/Eco_safe_driving.pdf 7 Delhomme, P et al. (2013). Self-reported frequency and perceived difficulty of adopting eco-friendly driving behaviour according to gender, age and environmental concern. Transportation Research Part D, 20. pp. 55 – 58. Savings per car life cycle Fuel Consumption CO 2 Emissions Savings Average Driver -6% -1,088kg £480 Top 10% -16% -2,895kg £1,260 Literature Review Motivational factors - Economic 4 - Environmental concern 4 - Embracing the challenge 5 - Road safety 6 Barriers to adoption - Perceived difficulty 7 - Lack of awareness 6 - Technological innovation is more interesting 2 - Lack of tuition in driving schools 6 Group most likely to eco-drive - Middle aged females with a high environmental concern 7 Group least likely to eco-drive - Young males and females 7 Michael Wilson Supervisor: Dr Daryl Hibberd Institute for Transport Studies Methodology Step 1 – Develop an online scenario based questionnaire divided into 4 sections Section 1 – Personal Characteristics: Identify the key variables such as age, gender, income and environmental concern. Section 2 – Strategic Decision Scenarios Examine the motivation for the selection of vehicle type, checking tyre pressure and fuel choice. Section 3 – Tactical Decision Scenarios Examine the motivation for route choice regarding congestion , road terrain and whether individuals consider removing excess vehicle weight. Section 4 – Operational Decision Scenarios Examine the motivation for driver speed, driving style, air conditioning use and idling. Example Question You are given the choice of two routes to the same destination. Route 1 will take 1 hour and emit 5% more CO2. Route 2 will take 10 minutes longer, emitting 5% less CO2. Which do you choose? Step 2 – Obtain 100+ respondents to achieve a representative sample of the population. Respondents will be private vehicle users with a valid driving license. Step 3 – Analyse results by performing a regression analysis between variables in SPSS to answer research questions. Possible Further Research Examine how eco-driving campaigns can target driver groups through motivational factors and removal of barriers Attempt to quantify the environmental impact if eco-driving was implemented as a soft policy measurement Undertake a cost-benefit analysis to calculate the NPV to the driver of eco-driving Aims and Objectives The aim of this project is to explore the relationship between individual characteristics, eco- driving behaviours and motivational factors. The following objectives have been set to answer the overarching question of who eco-drives and why? Identify motivations behind eco-driving Identify which drivers are most likely to undertake eco-driving behaviour Identify whether individuals are conscious of eco-driving Identify the potential barriers to eco-driving Hypotheses The following hypotheses will be tested in order to provide a structure and focus to answer the project objectives. The strongest motivation to eco-drive is fuel conservation Lower income groups are more likely to eco-drive because of economic influence Individuals who travel a greater distance are more likely to eco-drive because of the experience they have gained The greatest barrier is awareness of eco-driving practices
- 58. How does statedpreference design affect the valuation of ‘soft’ factors? Introduction Although there is a wide literature about the valuation of time, congestion and other significant issues such as price elasticities, there is a lack of confidence in the valuation of soft factors. Soft factors are seen as attributes of secondary importance in demand choice but still effect demand. Soft factors for train passengers can be split into two main groups; • On-board: Seat comfort, noise, information, security etc. • Off-board: shelters, seating, CCTV, passenger lounge, heating, lighting etc. It is important to value these attributes correctly to make sure that there is not an over or under valuation, resulting in train operating companies adapting their rolling stock or railway stations based on implausible fare augmentations. Objectives The objective of this dissertation is to analyse how different stated preference designs affect the choices that respondents make. There is wide spread tolerance of implementing stated preference surveys that are designed in such a way that it is easy for respondents to judge the aims of the surveys. This opens the survey design up to respondents’ strategic bias. • Does the design of a stated preference survey affect the valuations of the soft factors; • Identical valuations- zero strategic bias • Different valuations- strategic bias may be present where respondents can ‘play’ the survey Literature Review • Values of attributes can be 3 times higher when the purpose of the study is transparent, and thus there can be strategic bias (Wardman and Whelan 2001) • Direct valuations provided for soft variables are often unconvincing (Bates 1994). • Soft factor values across 18 different UK studies appear to be too large and have a lot of variation (Wardman and Whelan 2001). • By adding up to 10 soft factors, passengers state they are willing to pay double the original fare. This is very unrealistic in real life (Bates 1994). • It is necessary to implement an upper valuation cap for a bundle of soft factor improvements to stop it becoming unattainably high (Steer Davis Gleave 1990). • Direct valuations of soft factors vary with the number of factors included in the survey (Bates 1994). Methodology and Data Collection • Eight different designs will be made using Biogeme; • Three transparent survey designs and five mixed designs. • Data will be collected in face to face interviews on First Transpennine Express services. • It is hoped that there will be 1000 respondents over a five day surveying period. • Surveys will be initially done on paper, with the choices being analysed using a range of computer packages. Key Questions To Be Answered • Does a transparent survey design give higher soft factor valuations? • Does the ordering of positive and negative attributes in the choice question affect the soft factor valuations? • Are there different soft factor valuations when using fare and time as numeraires as opposed to only using one numeraire per survey design? Attributes Alternative One Alternative Two Seats Current condition Reupholstered Air Conditioning Current availability Individual air conditioning Noise As now Quieter service Ease of access onto train As now (steps) Train door level with platform Time 30 minutes 45 minutes Choice Attributes Alternative One Alternative Two Seats Current condition Reupholstered Air Conditioning Individual air conditioning Current availability Noise As now Quieter service Ease of access onto train Train door level with platform As now (steps) Time 45minutes 30minutes Choice Other Design Combinations 1. Time: Grouped positive and negative attributes 2. Fare: Transparent 3. Fare: Grouped positive and negative attributes 4. Fare: Each row opposite to the previous 5. Time and cost: Transparent 6. Time and cost: Each row opposite to previous References • BATES J, 1994, Reflections on Stated Preference: Theory and practice, Chapter 6 of Travel behaviour research: updating the state of play, pp.89-103 • STEER, DAVIES, GLEAVE, 1990, The effects of quality improvements to public transport, Wellington regional council • WARDMAN, M., WHELAN G., 2001, Valuation of improves railway rolling stock: A review of the literature and new evidence, Transport Reviews, Vol 21, No.4, pp415-447
- 59. COMPARISON BETWEEN CAROWNERSHIP CHARACTERISTICS IN UK & JAPAN The difference in socio-economic profile between UK & Japan potentially has an effect on car ownership characteristics between the two countries at household level. It is interesting to look at some attributes which is significant to car ownership level in the two countries. The study is conducted to develop a model based on basic regression analysis and discrete choice principles, which is an advanced regression technique, to explain the relationship between socio-economic attributes and car ownership as dependent variable. UK Socio-economic data for UK is taken from UK National Census data as distributed by UK Data Archive. Full census in the UK is conducted every 10 years by Census Division of Office for National Statistics. JAPAN Information on Japanese socio- economic profile is provided through Japanese General Social Survey which was conducted in 2005. This survey was designed and conducted by Osaka University & Tokyo University. ABOUT THE DATASETS Some other published statistics are also used in this dissertation, including National Travel Survey, World Bank Statistics and Organisation for Economic Cooperation and Development (OECD). KEY STATISTICS SUMMARY CHOICE MODELLING BASIC REGRESSION The analysis is still being carried out for both datasets. Given in the table below is the most recent estimation for JGSS data 425 cars/ 1000 people 441 cars/ 1000 people Based on World Bank Statistics, there are 425 cars/1000 people in UK in 2001, and 441 cars/1000people in Japan in 2005.. $$$$$$ $$$$$ US $ 27,926 Annual income per head US $ 25,616 Annual income per head According to Organisation for Economic Co-operation and Development (OECD) published statistics in 2011, annual income per head in UK is 3% higher than in Japan. 78.4% employed 61.2% employed It is revealed that 78.4% of respondents in UK were in employment, while 2.2% did not have a job. Students were 0.5%. Whilst in Japan, 61.2% of the respondents were employed, and 37.1% were unemployed . UK NATIONAL CENSUS DATA 2001 Sample Size 2,964,871 Respondents’ age ranging between 0 – 85+ Car ownership is given for 0, 1, and 2+ cars Main commuting mode is given Employment status is given with 8 class NSEC system Income information will be imputed from other source (National Travel Survey, 2001) Communal 19.4% + 41.3% 37.4% 1.8% Don’t have cars CAR OWNERSHIP AGE 41% of the respondent owns a car and 37% owns more than 2 cars. Whilst 19% do not own cars, and the remainder use cars in communal, this includes car sharing. MAIN COMMUTING MODEThe main commuting mode in UK was dominated by car with the proportion of 61%, while walking takes 12% in 2001 CAR OWNERSHIP The minimum cars owned is determined by the total number of car types chosen by respondents, while the maximum car owned takes the number of adult into account. In Japan, the car license is issued to individuals aged over 18 . Only respondents 20 years old and older who participated in the survey AGE COMMUTING MODE In this survey, the respondents are allowed to choose more than one mode. Car is dominating as chosen by 543 respondents, 95% of which choose car only. Dissertation by: Rendy Prakoso MSc(Eng) Transport Planning & Engineering Postgraduate Student ts13rwp@leeds.ac.uk Supervisor: Professor Stephane Hess University of Leeds, UK Professor Nobuhiro Sanko Kobe University, Japan COMMUTING TIME (MIN) JAPAN GENERAL SOCIAL SURVEY 2005 Sample Size 1,872 Respondents’ age ranging between 20-89, Car ownership is determined by type of car and age of respondent (licence issued for age 18 and over) Household members age and sex information are included Income is given in 19 categories, with 714 missing (38% of the data) ACKNOWLEDGEMENT Dependent variable = car ownership Independent variable = age, sex, income, employment, commuting mode, commuting costs (time/distance) Utility function U n,j = V n,j + εn,j j = different levels of car ownership Vn,j = f (β, x n,j) Logit model, choice probabilities The Japanese General Social Surveys (JGSS) are designed and carried out at the Institute of Regional Studies at Osaka University of Commerce in collaboration with the Institute of Social Science at the University of Tokyo under the direction of Ichiro TANIOKA, Michio NITTA, Noriko IWAI and Tokio YASUDA. The project is financially assisted by Gakujutsu Frontier Grant from the Japanese Ministry of Education, Culture, Sports, Science and Technology for 1999-2008 academic years, and the datasets are compiled and distributed by SSJ Data Archive, Information Center for Social Science Research on Japan, Institute of Social Science, the University of Tokyo. β β β β β β β β β β β
- 60. Objectives • To examinewhether transport infrastructure investment causes economic growth and vice versa, • To examine whether transport infrastructure investment causes employment and vice versa, • To examine whether transport infrastructure investment causes labour productivity and vice versa -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 1980 1990 2000 2010 2020 GDP Year Annual GDP Growth (%) Expectations There is a positive dual relationship between transport infrastructure investment, economic growth, employment and productivity. Conclusion, Transport investment is a necessary but not a sufficient condition for economic growth Hypothesis There is: • Unidirectional Granger- causality from TINFI to GDP. • Unidirectional Granger- causality from GDP to TINFI. • Bidirectional (or feedback causality). • Independence between TINFI and GDP. The same procedure is repeated for each of the other variables Problem Poor Transport infrastructure constrains growth. Less than 3 percent of GDP has been invested in transport over the years. Roads subsector carries 96.4 per cent of the total freight yet only 16 percent of it is paved, only 68 percent is in good condition. Only 26 per cent of the rail network is functional. Only one wagon ferry vessel on L. Victoria and only one functional international airport Transport Infrastructure Investment and Economic Growth: The Case of Uganda Richard Sendi MA Transport Economics Student 2013/14 Supervisor: Jeffrey Turner Background Transport is the pivot around which the wheel of every economy revolves. It enables growth as it stimulates investment, lowers costs of doing business, opens up new opportunities, improves productivity and access to social services. Transport investment and economic growth do influence one another Methodology A Granger (1969) causality approach will be used to determine the relationship between transport investment, economic growth, employment and productivity. (𝐺𝐺𝐺𝑔) 𝑡 = 𝛼 + ∑ 𝛽𝑖(𝐺𝐺𝐺𝑔 𝑚 𝑖=1 ) 𝑡−𝑖 + ∑ 𝛶𝑗 𝑛 𝑗−1 (𝑇𝐼𝐼𝐼𝐼𝑔) 𝑡−𝑗+ û 𝑡 (𝑇𝑇𝑇𝑇𝑇 𝑔) 𝑡 = µ + ∑ 𝜌𝑖(𝐺𝐺𝐺𝑔 𝑚 𝑖=1 ) 𝑡−𝑖 + ∑ 𝛹𝑗 𝑛 𝑗−1 (𝑇𝐼𝐼𝐼𝐼𝑔) 𝑡−𝑗+ ě 𝑡 Data from World Bank, and Uganda Bureau of Statistics
- 61. Ghost islands takethe form of a dedicated traffic lane for vehicles turning right from the major road, at junctions under priority control, with non-physical separation provided by road markings to allocate road space. Priority T-junctions on single carriageway trunk roads in the UK with a daily average of more than 500 vehicle movements on the minor road are required to have a ghost island, as defined by the mandatory ‘Design Manual for Roads & Bridges’ (DMRB, TD 42/95). However, more recent guidance for non-trunk roads environments acknowledges that priority T-junctions without a ghost island "will often be able to cater for higher levels of turning traffic without resulting in significant congestion” (Manual for Streets 2). There is currently no national design guidance in the UK regarding the provision of ghost islands at junctions on the local highway network. This study will investigate, from the context of local highway networks, the two factors that design guidance indicates are the main considerations when deciding whether a ghost island is required: junction capacity and road safety. STATS19 road safety records for priority T-junctions with and without ghost islands will be statistically analysed, to identify any differences in road safety performance through assessment of relevant collision factors, such as vehicle movements, road user groups and contributory factors. It is acknowledged that the combination of usage, behaviour, geometry and environment is unique at all junctions, therefore road safety data will be analysed for junctions that are as comparable as possible, particularly with respect to: Traffic flow patterns, including major and minor road flow volumes, distribution ratios, proportions of large vehicles, and daily/hourly flow profiles; Route type (e.g. urban arterial, rural minor, inter-urban); and Use by other modes, such as pedestrians and cyclists. The research will look to cover a range of traffic flow volumes (low/medium/high). When should priority T-junctions include ghost island provision? Steven Windass (Supervisor: Haibo Chen) The impact of ghost island provision on junction capacity, vehicle delay and congestion will be assessed utilising predictive modelling software (PICADY and the underlying empirical formulae). Multiple combinations of junction layout and traffic flow patterns will be tested for scenarios with and without a ghost island, based on variables typical for urban priority T-junctions in the UK. Through consideration of the two branches of analysis and their relative importance, it is intended to define an indicative traffic flow threshold(s), or choice matrix, to inform the decision of whether to provide a ghost island at priority T-junctions on non-trunk roads. The impacts with respect to junction capacity and road safety will be assessed jointly through Cost-Benefit Analysis to understand the Net Present Value of ghost islands, considering the cost of traffic delay and Personal Injury Collisions (PICs) against the relative cost of ghost island construction at existing and proposed junctions. It is acknowledged within various highway design guides that the decision on whether to provide a ghost island should be based on consideration of all pertinent factors, not just capacity and road safety. Therefore, this study is intended to provide practitioners with research to inform the decision, particularly at preliminary design stages, but not to replace analysis of site-specific circumstances. Priority T-Junction Traffic Streams: PICADY Modelling q = stream traffic flow qc-b = traffic turning from Arm C to Arm B Great Britain, 2012: Road Length by Type Trunk Roads 7,508 miles 3% Local Highway Network 237,865 miles 97% TOTAL 245,373 miles
- 62. 6.0 7.0 8.0 9.0 10.0 11.0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Occupancy (buspassengerkmsper busservicekm) Year Average bus occupancyin Greater Manchester Growth in bus occupancy in Greater Manchester Dissertation for Master of Transport Planning 2014 Student: Vi Ong 200813163 Supervisor: Dr. Jeremy Toner Research Questions èSocial, economic, environmental or policy determinants of occupancy rate changes services in Greater Manchester èPossible future patterns of change in bus passenger occupancy or loading rates in Greater Manchester on local bus Research Context èDeregulation of local bus services in October 1986 èEnsuing ‘bus wars’ result in bus service provision (mileage/kilometreage) peaking at 40% above pre- deregulation level. Yet passenger use (patronage) over the same period declines by 30%. èSince 1992, bus usage (in passenger kms) has fallen by 17%, yet bus occupancy (passenger kms per service km) has increased by 19%. Project Context This topic was proposed by Transport for Greater Manchester (TfGM). TfGM is responsible for the delivery of transport infrastructure and services in Greater Manchester, and the implementation of transport policy set by the Greater Manchester Combined Authority. This dissertation is intended to provide greater assurance for TfGM in the assumptions applied during planning for: èstop and terminal capacity at locations with high bus volumes; and èthroughput capacity on corridors or roads with high bus volumes in Greater Manchester. Data Manchester-specific historic and current data, relating to patronage and service provision, will be supplied by Transport for Greater Manchester (TfGM). Exclusive TfGM data sets to be utilised include the: èContinuous Passenger Survey (CPS); and èManual Bus Survey. Other data sources to be utilised in comparisons with other case studies include reports from other transport authorities or departments, academic journals or papers, and trade/industry journals. Risks The project is heavily reliant on the use of secondary data. Issues may arise as a result of: èInconsistency in methodology for collection or aggregation of data between different datasets or sources; èCommercial-in-confidence data that cannot be published in the dissertation; or èUnavailability of data or insignificant sample sizes for particular metrics. These risks can be mitigated by applying sensitivity testing with externally-sourced data where Manchester-specific data is not available. Other risks pertain to time and resource availability. Vi Ong, 2014.Piccadilly Gardens Bus and Metrolink stations, Manchester Methodology èReview of local bus public transport services èLiterature review of similar studies to inform the development of potential hypotheses and variables to be tested and compared èStatistics review from publicly-accessible sources such as Office for National Statistics, Department for Transport, TfGM and academic literature èWorking to utilise exclusive TfGM in-house data resources èDis-aggregate and aggregate statistics from different sources to provide common basis for comparison (e.g. location, time, etc.) èComparisons to identify trends, including statistical analysis 200 250 300 350 400 450 500 550 600 40 50 60 70 80 90 100 110 120 1975-1976 1977-1978 1979-1980 1981-1982 1983-1984 1985-1986 1987-1988 1989-1990 1991-1992 1993-1994 1995-1996 1997-1998 1999-2000 2001-2002 2003-2004 2005-2006 2007-2008 2009-2010 2011-2012 Patronage (millionpassengertripsperyear) Serviceprovision (millionbusmilesoperatedperyear) Financial Year Service provision and patronage and in Greater Manchester Service provision Patronage (unadjusted) Patronage (adjusted) Data reproduced with permission from TfGM 2014, Email to Vi Ong, 23 April. ‘Adjusted’ patronage data is due to change in methodology adopted by TfGM.
- 63. There are 2main objectives of this study • Understand how to encourage old people in Taiwan to use electric bikes instead of normal scooter • Analyze how to encourage old people who only walk or use public transportation to use electric bikes, to increase their accessibility Objectives Analysis of existing data To know Taiwanese’s attitude toward electric bikes, i.e. How do they think of the advantages and disadvantages of electric bikes in general Empirical work • Sample: old people in Taiwan’s suburban • Questionnaire: Older people’s travel behaviour The main questions would include: Subject’s age and gender, how often do they travel, what vehicles do they use… The factors that affect older people’s willingness What factors affect the willingness of them to change their habit more Data analysis • Cross-analyse the data. Summary and conclusion With the data analyzed, we can conclude what are the more practical ways to encourage older people to use electric bikes Methodology Analysis of existing data To know Taiwanese’s attitude toward electric bikes, i.e. How do they think of the advantages and disadvantages of electric bikes in general Empirical work • Sample: old people in Taiwan’s suburban • Questionnaire: Older people’s travel behaviour The main questions would include: Subject’s age and gender, how often do they travel, what vehicles do they use… The factors that affect older people’s willingness What factors affect the willingness of them to change their habit more Data analysis • Cross-analyse the data. Summary and conclusion With the data analyzed, we can conclude what are the more practical ways to encourage older people to use electric bikes Methodology Taiwan at a glance Capital: Taipei Population: 2.3million (2014) Rural population: 5.2% (2012) Total land area: 36,193 km² GDP total: $517.019 billion (2013) GDP per capita: $22,002 (2013) In Taiwan, • The number of scooters tripled the number of cars • Lots of older people uses scooters • This indicates that there is a big potential for scooter users to change to use electric bikes In recent years, the number of electric scooters in Taiwan has increase dramatically, but the number of electric bikes has decrease Background Private passenger Cars Motorcycle and scooters Total registered number 5,909,115 15,139,628 Total people over 60 with license 1,739,658 2,063,130 How to encourage older people to use electric bikes in Taiwan? Yu-Hsuan Liu , Supervisor: Frances Hodgson There are two expected outcome of the study: • Identify the factors that would encourage older people to use electric bikes • Provide reference for future policy changes Outcome 0 5000 10000 15000 20000 25000 30000 2006 2007 2008 2009 2010 2011 Electric bikes Electric scooters Source: Data used from Taiwan Industrial Development Bureau - Electric scooter industry department Website (2014) The advantages of electric bikes • Help older people exercise • Can use electric power when facing hills • No emission • Less costly Electric bikes advantages