Journal article
A Model of Ridesourcing Demand Generation and Distribution
PS Lavieri, FF Dias, NR Juri, J Kuhr, CR Bhat
Transportation Research Record | SAGE PUBLICATIONS INC | Published : 2018
Abstract
Ridesourcing has experienced exponential growth in recent years, yet its impact on individual travel are unclear and have not been adequately examined. Recently, an Austin-based ridesourcing company released a large dataset containing disaggregate trip-level information. In this research, we use this new dataset in tandem with several publicly available data sources to estimate two models: a spatially lagged multivariate count model, which is used to describe how many trips are generated in a specific zone on both weekdays and weekend days; and a fractional split model, which helps us identify the characteristics of zones that attract ridesourcing trips. Our results show spatial dependence i..
View full abstractGrants
Funding Acknowledgements
The authors would like to thank RideAustin for sharing the data publicly. This research was partially supported by the U.S. Department of Transportation through the Data-Supported Transportation Operations and Planning (D-STOP) Tier 1 University Transportation Center. The first author acknowledges funding support from CAPES and the Brazilian Government, and the fifth author acknowledges support from a Humboldt Research Award from the Alexander von Humboldt Foundation, Germany. The authors are grateful to Lisa Macias for her help in formatting this document, and to three anonymous referees who provided useful comments on an earlier version of the paper.