Inaugural Professorial lecture by Simon Shepherd, Professor of Choice Modelling & Policy Design. Institute for Transport Studies, University of Leeds, 9th September 2014. For audio recording see: www.its.leeds.ac.uk/about/events/inaugural-lectures2014 www.its.leeds.ac.uk/people/s.shepherd www.its.leeds.ac.uk/research/themes/dynamicmodelling

1. Institute for Transport Studies
FACULTY OF EARTH AND ENVIRONMENT
Inaugural Lecture:
Professor Simon Shepherd
How can modelling help resolve
transport challenges?

2. Outline
• Signals and bus priority
• HOV lanes
• Road Pricing
• Strategic models – system dynamics
• Greenhouse Gas reduction
• Electric Vehicle take up
• Challenges

3. Social dilemmas
Dawes (1980)
“Social dilemmas are characterised by two
properties:
(a) The social payoff to each individual for
defecting behaviour is higher than the
payoff for cooperative behaviour
(b) All individuals in society receive a lower
payoff if all defect than if all cooperate”

4. Transport is a form of social
dilemma

5. Early days

6. Quinn, Montgomery,
May 1988
Empirical study of traffic
control in Bangkok looking at
queue management versus
manual (police) control.
• Over-saturated conditions
called for new strategies
• Key was to avoid blocking
back during green phase
• Automatic signals were
seen to be 6% better in
terms of delay than police
control.
• Happy police could go
home half an hour early!

7. Data collection
All done without “big data”
Iterative process between
data and model

8. My PhD thesis
Based on Ramp metering approach
by Papageorgiou in Paris.
Developed in micro-simulation and tested
In field in Leeds and Turin with two real
Systems – SCOOT and SPOT

9. On Site in Turin

10. Adapted to grid
networks
Gridlock prevention strategy
35% reduction in delay

11. On the Box
Simon Box -can humans do
better than signal controllers?
BBC the One Show 2013
Simple experiments seem to
suggest that Humans can do
better in simple cases

12. San Francisco 2013
Fig. 2 The test site of Downtown San Francisco: (a) real network; (b) simulation model; (c) partitioning of the network into 3
reservoirs.
Also saw between 10-40% reduction in travel times – but note problems in 1970s
with this in Nottingham zone and collar experiment
Konstantinos Aboudolas , Nikolas Geroliminis. Perimeter and boundary flow control in multi-reservoir heterogeneous networks
Transportation Research Part B: Methodological, Volume 55, 2013, 265 - 281

13. Reflection on thesis
Three future situations:
(1) Network efficiency through traffic
responsive signals with auto-gating for
over-saturated periods
(2) Improve network efficiency and
manage demand with road pricing
(3) Improve network efficiency for public
transport with priority at signals whilst
creating delays for private car to
restrict demand for car use
“It is the author’s belief that
concentrating on the short term
benefits of strategies restricts the
initial scope of strategies to be
investigated… ignores long term
impacts of chosen strategies”
Shepherd (1994)

14. Look elsewhere for inspiration
“PIG DATA”

15. PRIMAVERA – first for
Leeds
Field trials of bus priority and
queue management
strategies in Leeds +Turin.
Both systems improved bus
times by 10% in Leeds
SPOT also reduced car travel
times by 11-30% in Turin
Model under-estimated
savings compared to field
trials.
SPOT now in over 30 cities in
Europe
SCOOT has other methods
of gating and bus priority

16. High Occupancy Lanes

17. Another first for Leeds

18. Leeds modelling
Used SATURN network model to
explore various scenarios
Diverted about 16% of traffic to
other corridors with little effect on
total network
We found that 3+ would be better
than 2+
Savings in real life were 2-3
minutes along the corridor –
confirming model results
We also tested a motorway
scheme as per Madrid which
gave negative benefits overall

19. Site visits are
important
Salzburg Austria
Lots of sources/sinks in the
data from existing model
Site visits essential when
modelling

20. Remember to think about
failure mode

21. Road Pricing

22. The judgmental approach to cordon
design
Most road pricing schemes use
cordons
• Designed using professional
judgment
But performance depends critically
on cordon location
• e.g. London: threefold difference
in economic benefits depending
on number, location of cordons,
screenlines
450
400
350
300
250
200
150
100
50
0
0 2 4 6 8 10 12
Peak Central Charge (£/cordon crossing)
Economic Benefit (£m per
annum)
Charge Structure A Charge Structure B
Charge Structure M Charge Structure N
Charge Structure T Charge Structure W

23. Edinburgh judgmental design
Inner cordon
1
Inner cordon
2
Outer cordon
Outer cordon
1
2

24. 18000
16000
14000
12000
10000
8000
6000
4000
2000
 
PACMAN network : Benefit and Langrangian value versus charge on link 2-4
0 10 20 30 40 50 60 70 80 90 100 110

0
Charge (seconds)
Total benefit (seconds)
  
D x dx     F  c 
 
F
ip p j jq
0
1 ( )
     
1 1 1 1 1 1






   
      
   



  
j j i iq q
ip p jp j jq
     






 

 

 

 

   

 

 

 


  
I
i
P
p
J
j
P
q
I
k
kq q
P
q
I
i
J
j
I
i
I
k
kq q
P
q
P
p
jp
F
i i i
c F f D F
P
p
ip p
1 1 1 1 1 1
Total benefit
Langrangian curve
Second best – optimisation
approach

25. GA
Competitive environment
Mutation

26. Look for short cut
Aim to develop a method
between judgement and GA
based approach but which uses
theory
Top 15 Marginal Cost tolls gave
high proportion of first best
benefits
Could this information be used in
designing a closed cordon?
Does it transfer to larger
networks?

27. Display SLA using bandwidths

28. Short cut
performance
Doubles benefit compared to
judgemental cordon
Achieved 93% of GA optimal
result
Transfers to other networks

29. Strategic models and system
dynamics

30. CLD example
Simple example
Eggs
Chicken
+
+
etc.
Time
Population
Reinforcing
feedback loop
+

31. CLD example 2
Simple example 2
Eggs
Chicken
+
+
+
# Road
crossing
+
-
Population
Balancing
feedback loop
etc. Time
-

32. Stocks and flows
Stock
inflow outflow
t
     
Stock t Inflow s outflow s ds Stock t
( ) ( ) ( ) ( ) 0
t
0

33. eggs
+
Chickens
+
deaths births
+
road crossings
+
-
Chickens
1,000
500
0
0 2 4 6 8 10
Time (Month)
Chickens : with crossings
Chicken and eggs model
Note : 푑푒푎푡ℎ푠(푡) =
푟표푎푑 푐푟표푠푠푖푛푔푠(푡)2
1000

34. Simple population model
Population
births deaths
birth rate death rate
Population
800
400
0
0 20 40 60 80 100
Time (Month)
Rabbit
Population : Current
풑풐풑풖풍풂풕풊풐풏 = 풃풊풓풕풉풔 − 풅풆풂풕풉풔
풅풆풂풕풉풔 = 풑풐풑풖풍풂풕풊풐풏 ∗ 풅풆풂풕풉 풓풂풕풆
풃풊풓풕풉풔 = 풑풐풑풖풍풂풕풊풐풏 ∗ 풃풊풓풕풉 풓풂풕풆
average time in young
Population
Young
average time in middle average time in old
births aging young
birth rate
Population
Middle
Population
Old
aging middle aging old
initial pop
infant
initial pop
middle
initial pop
old

35. Fox
Population
fox births fox deaths
fox food availability
fox birth rate
fox food
requirements
average fox life
rabbit birth rate average rabbit life
fox consumption
of rabbits
initial fox
population
fox mortality
lookup
Rabbit
Population
rabbit births
rabbit crowding
carrying capacity
initial rabbit
population
effect of
crowding on
deaths lookup
fox rabbit
consumption
lookup
rabbit deaths
Rabbit Population
4,000
2,000
0
0 10 20 30 40 50
Time (Year)
Rabbit
Rabbit Population : Current
Fox Population
200
100
0
0 10 20 30 40 50
Time (Year)
Fox
Fox Population : Current

36. MARS is a Land Use Transport
Interaction model
Transport sub-model
Land use residential
location sub-model
Land use workplace
location sub-model
Accessibility
Rent, Land price, Available land
Spatial distribution
residents
Spatial distribution
workplaces

37. Basis of MARS
Means of transport
(Use) Car
FUR
PT
Slow
Core city
Car
PT
Slow
Uses*
Built up structure Transport structure
* Residing, leisure, etc.
Uses*
-
-
+
-
-
-
+
-
+
+
-
-
+
+

38. Advanced systems -
CITYMobil

39. Gateshead Tyne and Wear

40. Cybercar PT feeder
In 2035, introduction of cybercar
results in local impacts:
• Car– 8% peak decrease, 30% off
peak decrease
• Bus– 36% peak decrease, 50% off
peak decrease
• Rail– 193% peak increase, 170%
off peak increase
• Slow- 29% peak decrease, 45%
off peak decrease

41. EU Level model

42. Integrated assessment of policy scenarios:
GHG-TransPoRD modelling approach
Energy prices
Biofuel supply
Energy investment
GDP
Transport demand
Transport energy demand
POLES
World energy model
Energy prices
Policy
scenario
Technology by
mode
Investment in
R&D and new
production
National
policies
Urban policies
ASTRA
Integrated economy-transport-
environment
model
TREMOVE
Environmental impact
model and vehicle
fleet model
MARS
Urban land use and
transport model
Energy prices
Vehicle fleet
composition

43. Example W&C visionary
800000
700000
600000
500000
400000
300000
200000
100000
0
Behavioural impact
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
Tonnes of CO2
Total CO2 Emissions
Do-nothing
W&C Visionary No Culture
Change
W&C Visionary with Culture
Change
Urban policy is limited without some form of behavioural change!

44. Other Technology
scenarios
Note REF case index 104
Max efficiency and market led
EV – Electrification
Hydrogen Fuel Cells take off
Ambitious technology with
strong transport policy
Optimistic technology scenarios
only get to a 55% reduction
target for 2050
Needs the behavioural
change with visionary policy
to achieve a 75% reduction

45. Example – uptake of
Electric Vehicles

46. Struben and Sterman (2008)

47. Sensitivity to word of mouth
Word of mouth between CV drivers is
crucial for success – as was marketing

48. Some of the conclusions
BAU assumptions are crucial!
Subsidies have no real impact in BAU
but are crucial in a failing market – but
expensive!
If EVs take off then we see significant
loss of fuel duty = £10bn p.a. 2050 in
most optimistic case.
Revenue preserver per vehicle could
range between £300-£650 p.a. by
2050.
A further 9% reduction in emissions
from CV gives similar results in terms
of CO2 at much lower cost to
government.

49. Supply Chain Disruption
Wilson (2007)

50. Highway maintenance
Fallah-Fini et al, (2010)
Load and
Area of the highway
deterioration factors
under distress
Highway
deterioration rate
Desired
maintenance budget
Budget allocated to
maintenance operations
Highway
improvement rate
+
+
+ - B1
Delay in
maintenance
+
R1
+
Maintencne
budget shortfall
+
Desired highway
condition
+
Available
maintenance budget
+ +
-
Maintenance Fix
Accelerated
Deterioration
+
Approach suggests less costly preventative maintenance rather than
more expensive (deferred) corrective maintenance should bring benefits to the system as a whole

51. Airline business cycles (Liehr
et al (2001)
A negative feedback loop with two
delays can result in cycles without any
growth.
Long delivery times and long aircraft life
mixed with need to maximise loading
causes cycles even without changes in
demand.

52. Bivona et al 2010 – Bus fleet
management example

53. Comparison of 2 scenarios
1. Reduce all budgets
Don’t replace retiring maintainers
Reduce training activities
Increase planned stoppages for
maintenance
Only replace buses which reach end
of life
2. Dispose of old buses now
But invest in some new buses.
Devote 15% time to train rookies
Reduce planned stoppages for
maintenance
Use out-sourcing

54. MacMillan et al (2014)
B1 – thought to be dominant loop – more cyclists more injuries – fewer
cyclists

55. Results for various
scenarios
Regional cycle networks/ self explaining roads – not enough to overcome
the safety in numbers or changing norm threshold. Arterial segregated
bike lanes more effective – note total serious injuries increase (top right) but
per cyclist reduced (bottom left).

56. Future challenges

57. How should models be
used?
Top down
process
Modelling
tools
Long
term
strategies
Bottom
up
process
Short
term
strategies
Signals – short term
HOV lane – more substantial change
But in these cases models were linked
with implementation
Road pricing – is this short term?
Strategic/longer term
GHG reduction, future systems, land
use etc
Needs collaboration between modeller
and decision maker!
Consider feedback between systems
and users at different levels

58. How should models be
used (2)?
Leaders
Decision
makers
Social/transport
dilemmas
Long term
impacts
Sum of
Individual
behaviour
Short term
symptoms
A match with social dilemmas
Need to change behaviour of
individuals and decision makers
Avoid short termism and fixes
that then fail

59. Change resistance as
the Crux -Harich (2010)
Social forces which favour change are
inter-linked with those which favour
resistance to change
The higher the leverage point the
higher the system will resist changing
it. (Donella Meadows 1999)
Changing agent goals scores most on
leverage point to solve the problem
Taxes and regulations score less well
on leverage point analysis
Suggests we need to work on
stakeholders and users together

60. Technology or behaviour
change?
Is there a resistance to change here?

61. And finally
“System dynamics helps us expand the boundaries of our
mental models so that we become aware of and take
responsibility for the feedbacks created by our decisions”,
Sterman (2002).

62. Policy 2014-15?
What’s the biggest transport policy this year?
Saved >£400
Budget impact 2013-14 KPMG calculator -£165

63. Thanks for listening
Any questions?