What drives the use of ridehailing in California? Ordered probit models of the usage frequency of Uber and Lyft
By F. Alemi, Giovanni Circella, Patricia L. Mokhtarian, S. Handy. Transportation Research Part C: Emerging Technologies, 2019
211 citations recorded by the source corpus.
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 population 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 millennials 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 distinguish 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 important 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.
Subfields: Automotive Engineering, Transportation
View Paper: https://www.semanticscholar.org/paper/11d9c62c33749f8a81edbeb4a65a9f874a55cfae
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Citations in the corpus
- Can sharing a ride make for less traffic? Evidence from Uber and Lyft and implications for cities (2021, background): “Numerous researchers have counseled intensifying the search for ways to increase pooling (Alemi et al., 2019; Alemi et al., 2018; Brown 2020; Narayanan et al., 2020; Shaheen 2018; Sperling et al. 2018; UCS 2020).”