Modelling the dynamics between tour-based mode choices and tour-timing choices in daily activity scheduling

The paper presents a dynamic discrete–continuous modelling approach to capture individuals’ tour-based mode choices and continuous time expenditure choices tradeoffs in a 24-h time frame. The analysis of traditional activity-based models are typically limited to activity-type, location and time expenditure choices. Besides, mode choice is often simplified to fit in a pre-defined activity schedule. However, decisions of tour departure time, tour mode choice and time expenditure choice for out-of-home activities are intricately inter-related, and common unobserved attributes influence these choices. This paper proposes a random utility maximization based dynamic discrete–continuous model for joint tour based mode and tour timing choices. Tour timing choice is modelled as continuous time allocation/consumption choice under 24-h time-budget. In the case of the tour-based mode choice component, it uses a modelling structure which harnesses the power of dynamic programming and discrete choice. A cross-sectional household travel survey dataset collected in the Greater Toronto and Hamilton Area in 2016 is employed for the empirical investigation in this study. Empirical model shows the capability of handling all possible mode combinations within a tour including ride-hailing services (e.g., Uber, Lyft). Empirical results reveal that individuals variations in time expenditure choice are defined by activity type, employment status, and vehicle ownership. In terms of mode choice, it is clear the emerging transportation service users have different travel pattern than conventional mode users. This modelling framework has the potential to test a wide range of policies.