Temporal Transferability of Model Components within an Activity-Based Travel Demand Modelling Approach

Abstract

Temporal transferability is one of the key and implicit conditions of developing travel behaviour models as these are developed for forecasting future levels of transport demand. Current travel demand modelling practices are focusing on the development of advanced models that better explain current behaviour rather than an emphasis on the ability of these models in forecasting. The importance of producing high temporal transferability of models should not be neglected as these models are used by planners and policy-makers to test policy decisions and to estimate future demands, which are critical inputs for infrastructure planning and maintenance. This research presents an investigation of the temporal transferability of activity generation process, scheduling process, and mode choice models. Three repeated cross-sectional household travel survey datasets collected in the greater Toronto and Hamilton area (GTHA) are used for the investigation. A multiple discrete-continuous extreme value model is used to develop an activity-travel generation model, and separate models are estimated for non-workers and workers. A Random utility maximization (RUM) based dynamic activity scheduling model is utilized to develop an activity-travel scheduling model for non-workers and workers separately. Heteroskedastic GEV model (HET-GEV) and HET-GEV model with scale parameter parameterized as a function of entropy are developed for commuting mode choice trips. Models are developed for individual years and then for the pooled data set of three cross-sectional years to develop a Meta model for each component. Individual year-specific models are used to increase knowledge about the temporal stability of different parameters of the model so that the Meta model could capture the non-linear evolution of some key parameters of the model. Different transferability indices are used to test temporal transferability of cross-sectional year-specific models and the Meta model. Results demonstrate an approach of effectively using multiple repeated cross-sectional datasets as pseudo panel data to develop Meta models to improve the temporal transferability of activity-based travel demand models.

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