Contents lists available at ScienceDirect Transportation Research Part C journal homepage: www.elsevier.com/locate/trc What drives the use of ridehailing in California? Ordered probit models of the usage frequency of Uber and Lyft Farzad Alemi a, ⁎ , Giovanni Circella a,b , Patricia Mokhtarian b , Susan Handy a a Institute of Transportation Studies, University of California, Davis, 1715 Tilia Street, Davis, CA 95616, United States b School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, GA 30332, United States ARTICLEINFO Keywords: Uber/Lyft Ridehailing Travel behavior Frequency model Ordered probit model with sample selection Zero-inflated probit ordered model with correlated error terms ABSTRACT The availability of ridehailing services, such as those provided by Uber and Lyft in the U.S. market, as well as the share of trips made by these services, are continuously growing. Yet, the factors affecting the frequency of use of these services are not well understood. In this paper, we investigate how the frequency of use of ridehailing varies across segments of the California po- pulation and under various circumstances. We analyze data from the California Millennials Dataset (N = 1975), collected in fall 2015 through an online survey administered to both mil- lennials and members of the preceding Generation X. We estimate an ordered probit model with sample selection and a zero-inflated ordered probit model with correlated error terms to dis- tinguish the factors affecting the frequency of use of ridehailing from those affecting the adoption of these services. The results are consistent across models: sociodemographic variables are im- portant predictors of service adoption but do not explain much of the variation in the frequency of use. Land use mix and activity density respectively decrease and increase the frequency of ridehailing. The results also confirm that individuals who frequently use smartphone apps to manage other aspects of their travel (e.g. to select a route or check traffic) are more likely to adopt ridehailing and use it more often. This is also true for long-distance travelers, in particular, those who frequently travel by plane for leisure purposes. Individuals with higher willingness to pay to reduce their travel time use ridehailing more often. Those with stronger preferences to own a personal vehicle and those with stronger concerns about the safety/security of ridehailing are less likely to be frequent users. These results provide new insights into the adoption and use of ridehailing that could help to inform planning and forecasting efforts. 1. Introduction The rapid expansion of digital technology, and in particular the increased availability of locational data and smartphone ap- plications, as well as the emergence of new technology-enabled transportation services, are transforming transportation demand and supply. New technologies and reinvented business models disentangle access to transportation services from the fixed cost of auto ownership by providing unique opportunities for the introduction and extensive deployment of a wide range of new transportation services. New mobility services range from car-sharing, including fleet-basedround-trip and one-wayservices such as Zipcar and Car2Go, respectively, or peer-to-peer services such as Turo, to ride-sharing services, including dynamic carpooling such as Carma and ridehailing https://doi.org/10.1016/j.trc.2018.12.016 Received 19 May 2018; Received in revised form 22 December 2018; Accepted 31 December 2018 ⁎ Corresponding author. E-mail addresses: falemi@ucdavis.edu (F. Alemi), gcircella@ucdavis.edu (G. Circella), patmokh@gatech.edu (P. Mokhtarian), slhandy@ucdavis.edu (S. Handy). Transportation Research Part C 102 (2019) 233–248 0968-090X/ © 2019 Elsevier Ltd. All rights reserved. T