AITPM presentation

1. The Complexities and Challenges
Developing Models Across Urban and
Rural Environments
Presenter: James Parrott
Wednesday 13 August 2014

2. • Urban and Rural Models
– Cover a large area beyond metropolitan
boundaries
– State, County, Country
– Usually at a higher level (less fine grained
detail)
• Relevant Models developed in Australia and
Sri Lanka
– Established Economy with a lot of existing
infrastructure
– Developing country with need for new
infrastructure to support growth
Urban and Rural Modelling

3. • Why build models outside of metropolitan cities?
– Low Daily Traffic Volumes
– Little Public Transport
– Congestion………Not Likely!
The Need for Rural Models

4. • Australia – growing importance of
cost-benefit analysis for proposed
infrastructure projects
– No model: back of the envelope
forecasts
– State/National Model:
• allows testing of various networks, demand
scenarios etc.
• useful to understand the impact of broad
regional policy
• can assess impact of new mining operations
The Need for Rural Models

5. • Developing Countries such as Sri Lanka
– Infrastructure is poor in comparison
to countries such as Australia
– Growing middle class =
Growth in car ownership
– Large infrastructure projects
earmarked to support economic growth
– Toll Roads will play a large role in enabling the
infrastructure to be built
The Need for Rural Models

6. • Zoning System
– Largely based on demographics and land use
data availability (current & future)
• Household Travel Survey
– Metropolitan
– May cover large rural towns
• Traffic Counts
– Annual vs Seasonal
– Public Holidays / Seasonal Weather
Data Availability

7. • Standard 4-Step approach to developing the model
– Multi-modal but not in traditional sense
 Keep it simple, focus on private and commercial vehicles or
fixed mode shares
– Freight network multifaceted
 Unless you can obtain the relevant data, keep it simple.
 Use inputs from a dedicated Freight model or develop a
base demand matrix
Addressing Issues Encountered

8. • Network / Regional Coding
– Topography varies by geographical locations
 Assess regions as to whether they display
different characteristics such as lower
speeds and develop custom link attributes
– Metropolitan Network more complex and Zones produce higher trip
volumes
 Disaggregate zones – trade off with size, speed and complexity
 Introduce custom link types to accommodate higher volume
 Disaggregate the network – trade off with size and speed
Addressing Issues Encountered

9. • Distribution Model – Sensitivity to Changes
– Distribution models provide a form of
induced demand
– Makes sense in an urban environment
where avg trip length <10km
– What about rural trips where avg trip
length is more like 50+kms?
– In reality, when you make a trip one way, then you will almost certainly
make the same trip in the other direction i.e. at least two 50+km trips
– Rural travel has less choice i.e. do not have 4 shopping complexes to
choose from, thus may only make one trip to shops for the week
(multiple errands)
Addressing Issues Encountered

10. • Distribution Model – Sensitivity to Changes
 Trip Distribution model is too sensitive to network changes
 Small changes cause counter intuitive results / benefits
 Distribution model is fixed for each forecast year using the base future
network
 Changes in posted speeds, introduction of new road links, duplications
etc. do not cause changes in travel patterns
– When does the trip length change?
 Changes to land use data i.e. new jobs, new attractions will change
travel behaviour
Addressing Issues Encountered

11. • Distribution Model – Urban vs Rural
Travel Patterns
– Urban travel on avg <10km
– Rural travel on avg >50km
– Distribution model needs to
cover all zones and all travel
patterns and be adaptive to land
use changes
– Three different patterns, Urban
only, Rural only and Inter-
Regional (Urban-Rural)
Addressing Issues Encountered
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ProportionofTrips
Trip Cost
Urban Rural Inter-Regional

12. • Distribution Model – Urban vs Rural Travel Patterns
– Three different distributions that need to be combined
 Approach adopted was to prioritise key movements (Internal Urban and Rural)
 Use Urban and Rural
distributions for urban
only and rural only trips
respectively and
re-balance
inter-regional trips so
that Origin and
Destination totals are
maintained
Addressing Issues Encountered
Demand
Matrix
URBAN Zones RURAL Zones
URBAN Zones
(1) Use the Urban-Urban Trip Distrbution
Profile for all OD Pairs located in this group
of zones
(3) Requires a rebalancing of the Inter-
Regional distrbution to ensure that the row
and column totals align.
RURAL Zones
(3) Requires a rebalancing of the Inter-
Regional distrbution to ensure that the row
and column totals align.
(2) Use the Rural-Rural Trip Distrbution
Profile for all OD Pairs located in this group
of zones

13. • Using a Prior Matrix to overcome lack of data
– Quality survey data on rural travel behaviours is limited. In developing
countries it may be non-existent.
– Validation is based on using independent data, but assumes it is
possible to develop a suitable model from the available data
– What if you don’t have enough data to produce a model to the level of
accuracy required?
 Suggested approach to improve the accuracy of the model is to
develop/estimate a prior matrix
Addressing Issues Encountered

14. • Using a Prior Matrix to overcome lack of data
 Develop prior using count data to develop base
travel behaviour
 Use the prior matrix to adjust existing models or
calibrate
 Not ideal or preferred, but sometimes you need to
be creative
 Preferred approach to remove compromised data
is to validate to a different model year i.e. a
dataset which has not been used to
derive/estimate the model
 Based on assumption of lack of data or
unacceptably poor performance
Addressing Issues Encountered
Network,
Factored OD Survey
Matrices
Initial Assignment Skim Cost Matrices
Zonal Proportions of Pop'n
and Emp,
Factored OD Survey
Matrices
Develop Trip Ends Trip Ends by User Class
Factored OD Survey
Matrices,
Skim Cost Matrices
Estimate Friction Factors
Friction Factors by User
Class
Trip Ends by UC,
Skim Cost Matrices,
Friction Factors by UC
Trip Distribution Initial Demand Matrices
Network,
Initial Demand Matrices
Assignment Iteration 1
Skim Cost Matrices,
Screenline Intercept Files
Skim Cost Matrices,
Screenline Intercept Files
Matrix Estimation Estimated Matrices
Network,
Estimated Matrices
Assignment Iteration N
Skim Cost Matrices,
Screenline Intercept Files,
Assigned Network
Assigned Network Convergence Criteria Criteria Met: Yes/No?
Loo
p
NO
YES
STOP
ITERATION
N + 1

15. • Distribution is key to a robust urban and rural model
– Fixed distribution based on a given model year, base network and land
use dataset
– Urban and rural travel are not the same and must be provided with
different distribution profiles
• More Surveys Needed
– Household Travel Surveys in Rural towns
– Roadside Surveys: can act as
a supplementary-HTS
Conclusions