Modelling Departure Time and Mode Choice for Commuting in the Greater Toronto and Hamilton Area (GTHA): Evaluation of Dynamic Travel Demand Management Policies

This thesis develops econometric models of departure time and travel mode choice to evaluate dynamic transportation policies. Dynamic policies affect travel attributes differently throughout the day. Both departure time and mode choice are modelled with Random Utility Maximizing (RUM) Generalized Extreme Value (GEV) discrete choice models that capture systematic and random heterogeneity. Departure time is represented by a heteroskedastic generalized extreme value model (Het-GEV) with overlapping choice sets. Studying the Greater Toronto and Hamilton Area (GTHA), models are estimated using Revealed Preference (RP) household travel data from the 2006 Transportation Tomorrow Survey (TTS). Empirical models are used to evaluate dynamic transit and road pricing policies. Results indicate that the models are capable of capturing mode and time switching behaviour in response to peak pricing policies. To alleviate demand while maintaining transit mode share, a road charge and a moderate, flat, transit fare increase throughout the morning peak are recommended.