Modelling car trip generation in the developing world - the tale of two cities

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Simulation Based Data Fusion

Institute for Transport StudiesFACULTY OF ENVIRONMENTModelling Car Trip Generation in the Developing World: The Tale of Two Cities

Mr. Andrew Bwambale, ITSDr. Charisma F. Choudhury, ITSDr. Nobuhiro Sanko, Kobe UniversitySchool of somethingFACULTY OF OTHER1MotivationStudy ObjectivesStudy AreaDataModelling FrameworkResultsConclusions

Outline

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Data sources

Motivation

Models are key to understanding and solving complex transport problems; however, there are limitations imposed by data collection budget constraints in developing countries.

Could transferable models be a possible solution?

Besides transferability, what are the limitation of current trip generation models?Data shortages in the application contextPossible Endogeneity between car ownership and trip generation (Simultaneity)

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Study Objectives

How does the household car ownership affect the household car trip rate in the context of developing countries? (2) How can we account for the potential endogeneity in car trip generation models?

(3) How can we account for data limitations associated with modelling car trip generation? and (4) How transferable are the models between two cities that have similarity in socio-demographics? 4

Data sources

Study Area

Focus will be on spatial transferability between Nairobi and Dar-es-Salaam.

These areas are thought to have largely similar socio-demographics.

Household travel survey data collected by JICA from both cities has been used in this study.

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Data sources

Data

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Mobile phone CDR

Data

NairobiDar-es-Salaam

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Mobile phone CDR

Data

NairobiDar-es-Salaam

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Mobile phone CDR

Data

NairobiDar-es-Salaam

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Mobile phone CDR

Data

NairobiDar-es-Salaam

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Data sources

Modelling Framework

Four ordered response probit car trip generation models have been estimated for each city.Model 1 (Car trip generation models with car ownership as an explanatory variable);

Household socio-economic variablesIncluding # of carsyn*# of car trips

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Data sources

Modelling Framework

Model 2 (Car trip generation models without car ownership as an explanatory variable);

Household socio-economic variablesIncluding # of carsyn*# of car trips

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Data sources

Modelling Framework

Model 3 (Two stage models estimated sequentially; first stage - car ownership model and second stage - car trip generation model);

# of carsyn*# of car tripszn*Household socio-economic variablesIncluding # of cars

Stage 1Stage 1Stage 2Stage 2Stage 213

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Modelling Framework

Model 4 (Joint car trip generation and car ownership models Simultaneous BOP models);

# of cars# of car tripszn*Household socio-economic variablesIncluding # of carsyn*

The BOP model14

Data sources

Modelling Framework

Model 1Model 2Model 3Model 4Is car ownership data required in the estimation context?Is car ownership data required in the application context?

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Data sources

Results

Models 1 and 2

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Data sources

Results

Models 3 and 4

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Data sources

Results

Models 3 and 4 contd

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Data sources

Results

Overall goodness of fit measures

>>> or =0.370.20Trip generation component>>>19

Data sources

Results

Overall model spatial transferability (individual parameters are relatively transferable)

Better transferability in this directionNairobi models are better.(434.10)(733.38)(2655.00)(3474.48)(car ownership component)(trip generation component)20

Data sources

Conclusions

In both cities, car ownership has been found to have a statistically significant positive influence on car trip generation.Models 1, 3 and 4.

The problem associated with potential endogeneity in modelling trip generation and car ownership can be addressed using Model structures 3 and 4.

Model 3: Endogeneity due to variable omission.Model 4: Endogeneity due to variable omission and simultaneity.

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Data sources

Conclusions

Possible ways of addressing the lack of car ownership data for car trip generation modelling in the application context can be addressed using Model structures 2, 3 and 4, though Model structure 4 is a better option.

Though all the four models have most of their parameters individually transferrable between the two cities, none of the models is wholly transferrable between the two cities.

22Improvement of transferability scores

Treatment of missing data as a latent variable

Further research

23Questions?

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