The effect of crowding level information provision on the revealed route choice of transit riders

Abstract

This study relies on the unique revealed choice dataset to investigate the impact of crowding information provision on the route choices of smartphone navigation application users. Extensive processing steps are documented, and data validation is performed to ensure that the dataset is representative of the travel behavior in the Metro Vancouver region, as well as of the crowding conditions on its transit system. A mixed logit model is used for the analysis to account for the panel effect of the dataset. The estimates indicate that information about crowding has a meaningful effect on the travel decisions transit navigation application users make, with the increase in crowding lowering the chances of a route being selected. The identified effects of crowding are also comparable to the estimates that the other sources of revealed preferences on transit (like smart card records) provide. For example, it is found that the time multiplier is 2.23 for a crowded trip (100%+ vehicle occupancy) in a rapid transit vehicle like bus rapid transit or light rail, and that crowded trips on a regular bus are perceived as almost six minutes longer. The findings of this study should be of interest to both the research and the professional community, as it provides more accurate findings than those coming from stated preference surveys and simulations, which are subject to limitations like uncontrolled biases and potential errors. At the same time, it informs transit agencies about the effect of crowding information provision and can potentially facilitate the possibility of expanding that effort (e.g. ensuring higher accuracy and broader availability of the data).