To share or not to share? Revealing determinants of individuals’ willingness to share rides through a big data approach

Abstract

Ridesplitting has been widely recognised as a promising mobility mode for sustainable transportation, but its success largely depends on a sufficient number of passengers who are willing to share their rides. To uncover the determinants of willingness to share (WTS), prior studies typically relied on either individual-level survey-based or aggregate-level data-driven methods. To combine the former’s strength in capturing individual choice preferences and the latter’s advantage in utilising available multi-source big data, this study proposes a big data approach to modelling individual choices between the solo and shared options for each trip. To reconstruct the choice process, we leverage large-scale real-world trip records and propose a learning framework to not only retrieve the trip time and fare of the chosen option (solo or shared), but also impute the likely time and fare of the alternative option. These reconstructed trip attributes are then integrated with the sociodemographic, built environment and traffic features from other data sources. Finally, all these variables are fed into a random coefficient logit model to reveal passengers’ ridesplitting preferences. Through a case study of Manhattan, New York City, we reveal the spatiotemporal pattern of WTS and its determinants. Results show that WTS varies greatly across space and time. The time-fare trade-off is identified as the most essential factor, with the value of time revealed to be about $28-36/h. WTS decreases with longer trip distance/commuting time/distance to the urban centre, lower road speed, and higher speed fluctuation/bus station/crime density, but increases with a higher proportion of middle-class/female/young residents, residential land use and metro station. The proposed methodology can be used to explain and monitor WTS in a cost-effective way, complementing traditional survey-based methods to better design and promote ridesplitting services.