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The Complexities and Challenges Developing Models Across Urban and
Rural EnvironmentsPresenter: James Parrott
Wednesday 13 August 2014
• 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
• Why build models outside of metropolitan cities?
– Low Daily Traffic Volumes
– Little Public Transport
– Congestion………Not Likely!
The Need for Rural Models
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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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0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Prop
orti
on o
f Tri
ps
Trip Cost
Urban Rural Inter-Regional
• 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
• 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
• 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
Typical Data Sources for Model and Parameter Estimations
Network,Factored OD Survey
MatricesInitial 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 MatricesEstimate Friction Factors
Friction Factors by User Class
Trip Ends by UC,Skim Cost Matrices,
Friction Factors by UCTrip Distribution Initial Demand Matrices
Network,Initial Demand Matrices
AssignmentIteration 1Skim Cost Matrices,
Screenline Intercept Files
Skim Cost Matrices,Screenline Intercept Files
MatrixEstimation Estimated Matrices
Network,Estimated Matrices
AssignmentIteration NSkim Cost Matrices,
Screenline Intercept Files,Assigned Network
Assigned Network Convergence Criteria Criteria Met: Yes/No?
Loop
NO
YESSTOP
ITERATIONN+ 1
• 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
Recommended