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A behavioral choice model of the use of car-sharing and ride-sourcing services


There are a number of disruptive mobility services that are increasingly finding their way into the marketplace. Two key examples of such services are car-sharing services and ride-sourcing services. In an effort to better understand the influence of various exogenous socio-economic and demographic variables on the frequency of use of ride-sourcing and car-sharing services, this paper presents a bivariate ordered probit model estimated on a survey data set derived from the 2014–2015 Puget Sound Regional Travel Study. Model estimation results show that users of these services tend to be young, well-educated, higher-income, working individuals residing in higher-density areas. There are significant interaction effects reflecting the influence of children and the built environment on disruptive mobility service usage. The model developed in this paper provides key insights into factors affecting market penetration of these services, and can be integrated in larger travel forecasting model systems to better predict the adoption and use of mobility-on-demand services.

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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 (Grant No. DTRT13-G-UTC58). The last author would like to acknowledge support from a Humboldt Research Award from the Alexander von Humboldt Foundation, Germany. The authors are grateful to Mr. Neil Kilgren of the Puget Sound Regional Council for providing the data used in this research study, to Lisa Macias for her help in formatting this document, and to four anonymous referees who provided useful comments on an earlier version of the paper.

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Correspondence to Chandra R. Bhat.

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Dias, F.F., Lavieri, P.S., Garikapati, V.M. et al. A behavioral choice model of the use of car-sharing and ride-sourcing services. Transportation 44, 1307–1323 (2017).

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  • Ride-sourcing services
  • Car-sharing services
  • Bivariate ordered probit model
  • Market adoption and use of disruptive mobility services
  • Travel demand forecasting