James Parrott

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


• 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


• 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


• 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)


• 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


• 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)


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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

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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


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


• 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


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

James Parrott

Business

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