Trip chaining and tour mode choice of non-workers grouped by daily activity patterns

Transportation planners are increasingly looking to reduce drive-alone travel. However, the existence of complex tours could be a major barrier to the shift from drive-alone to public transport. This is particularly problematical for the non-worker segment of the population (most of whom are women and/or older adults). To better understand how socio-demographics, trip attributes, and land use pattern shape tour complexity and mode choices for individuals, this paper introduces a cluster-based approach to model tour complexity, trip chaining, and tour mode choice. Based on daily activity patterns and time-use, five non-worker clusters were identified from the Space-Time Activity Research (STAR) data for Halifax, Canada. Number of tours per day for all clusters was modeled using a Poisson regression model. Trip chaining was then modeled using an Ordered Probit model. Finally, tour mode choice was modeled using a Multinomial Logit (MNL) model. This framework utilizes home-based travel tours, the sequence of trips that begin and end at home, as the basic unit of analysis. Results show that socio-demographic characteristics and tour attributes are strong explanatory factors of travel behavior, consistent with existing literature. Also, urban form characteristics have significant influence on non-workers' travel behaviour and tour complexity. This paper also offers a typology of travel tours to account for different non-work travel purposes. The findings of this study will improve the future evaluation of transportation projects and land-use policymaking.