Publication Detail
Microsimulation for Travel Demand Forecasting: A Dynamic Model System of Household Demographics and Mobility
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UCD-ITS-RR-92-04 Research Report |
Suggested Citation:
Goulias, Konstadinos G. and Ryuichi Kitamura (1992) Microsimulation for Travel Demand Forecasting: A Dynamic Model System of Household Demographics and Mobility. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-92-04
The use of cross-sectional models in travel demand forecasting involves some fundamental problems. First of all, it is based on the untested assumption that cross-sectionally observed variations in travel behavior can be used as valid indicators of behavioral changes over time. Secondly, future values of socioeconomic and demographic input variables are obtained using "allocation" methods, which "post-processes" aggregate forecasts into "pseudo-dissaggregate" data. As such, the methods fail to effectively and accurately capture the internal relationships among these input variables. And thirdly, it does not properly represent response lags involved in long-term mobility decisions (e.g., residence location and car ownership).
An alternative travel demand forecasting system is described in this paper. The system consists of two components: a micro-simulator of household socioeconomics and demographics, and a dynamic model system of household car ownership and mobility. Each component comprises interlinked models formulated at the household level. Replicated in the socioeconomic and demographic micro-simulator, are interactions and causal paths that underlie lifecycle evolution of individuals and households. Simulation units evolve from year to year, experiencing marriages, divorces, births, deaths, and so forth. Employment, income, driver's license holding, education level, and household size and composition, are among the variables that are internally generated in the simulation. Most model parameters can be modified to represent different future growth paths.
Output from the micro-simulator is then used to predict household car ownership and mobility. Thus many explanatory variables that are exogenous in other forecasting models are endogenous in this model system. The mobility component is made up of a household car ownership model, trip generation models, a modal split model, and travel distance models. Most models are "dynamic" with lagged dependent variables and serially correlated errors. Future travel demand is simulated for each simulation year.
The parameters of the model system have been estimated using observations from 5 waves of the Dutch National Mobility Panel data, covering a period of four years between April 1984 through April 1988. Other sources of information, external to the Panel data, were also used to estimate key parameters. The availability of the large-scale panel data, has been essential for the development of the detailed demographic and mobility model components.
The model system is a complex and flexible forecasting tool with which a wide range of future scenarios can be examined to answer a variety of "what-if" questions. The model structure, data requirements, estimation methods, and assumptions are presented in this paper. Forecasting is performed for the Year 2010 and examples illustrating the system's predictive capability and limitations together with the advantages and disadvantages of dynamic microsimulation, compared to cross-sectional travel demand forecasting, are also presented.
An alternative travel demand forecasting system is described in this paper. The system consists of two components: a micro-simulator of household socioeconomics and demographics, and a dynamic model system of household car ownership and mobility. Each component comprises interlinked models formulated at the household level. Replicated in the socioeconomic and demographic micro-simulator, are interactions and causal paths that underlie lifecycle evolution of individuals and households. Simulation units evolve from year to year, experiencing marriages, divorces, births, deaths, and so forth. Employment, income, driver's license holding, education level, and household size and composition, are among the variables that are internally generated in the simulation. Most model parameters can be modified to represent different future growth paths.
Output from the micro-simulator is then used to predict household car ownership and mobility. Thus many explanatory variables that are exogenous in other forecasting models are endogenous in this model system. The mobility component is made up of a household car ownership model, trip generation models, a modal split model, and travel distance models. Most models are "dynamic" with lagged dependent variables and serially correlated errors. Future travel demand is simulated for each simulation year.
The parameters of the model system have been estimated using observations from 5 waves of the Dutch National Mobility Panel data, covering a period of four years between April 1984 through April 1988. Other sources of information, external to the Panel data, were also used to estimate key parameters. The availability of the large-scale panel data, has been essential for the development of the detailed demographic and mobility model components.
The model system is a complex and flexible forecasting tool with which a wide range of future scenarios can be examined to answer a variety of "what-if" questions. The model structure, data requirements, estimation methods, and assumptions are presented in this paper. Forecasting is performed for the Year 2010 and examples illustrating the system's predictive capability and limitations together with the advantages and disadvantages of dynamic microsimulation, compared to cross-sectional travel demand forecasting, are also presented.