UC Davis Institute of Transportation Studies

Emerging transportation Emerging transportation services are quickly changing the way individuals travel by expanding the set of transportation alternatives available for a trip, allowing for more flexibility in travel schedules and providing access to transportation without incurring the costs of auto ownership. One of the most rapidly growing shared-mobility services are ridehailing services, such as those offered by Uber and Lyft in the U.S. market. In this dissertation,  the author investigateed the factors affecting the adoption and frequency of ridehailing services and the impacts that these services have on different components of travel behavior using California Millennials Dataset, a rich dataset was collected in fall 2015 with a comprehensive online survey administered to a sample of 2400 California residents, including millennials (i.e. young adults born between 1981 and 1997) and members of the preceding Generation X (i.e. middle-aged adults born between 1965 and 1980).

To investigate the factors that affect the adoption of ridehailing, the author estimated several models that help assess the role of individual characteristics and residential location in affecting these choices. The results of two binary logit models confirmed that highly educated, older millennials are more likely to use on-demand ride services than other groups. The researcher also find that greater land-use mix and regional accessibility by car are associated with greater likelihood of adopting on-demand ride services. Respondents who report higher numbers of long-distance business trips and have a higher share of long-distance trips made by plane are also more likely to have used these services, as are frequent users of smartphone transportation-related apps, and those who have previously used taxi and carsharing services. Among various attitudinal factors that were investigated, individuals with stronger pro-environmental, technology-embracing, and variety-seeking attitudes are more inclined to ridehailing.

Further, the author expanded his analyses of the factors affecting the adoption of ridehailing through the estimation of a latent-class adoption model that captures the heterogeneity in individuals’ tastes and preferences. Users of ridehailing can be grouped into three well-defined latent classes, based on their individual and household characteristics, lifestyles and stage in life. The three distinct classes are: (1) a class that is largely composed of more highly-educated, independent (i.e. who have already established their household) millennials, who has the highest adoption rate. The adoption of ridehailing services for the members of this class is influenced by the frequency of long-distance leisure and business-related trips made by non-car modes. The adoption of ridehailing among the members of this group is higher if they live in high-quality transit neighborhoods. (2) The second highest adoption rate is observed among the members of the class that is mainly composed of affluent individuals living with their families who are either dependent millennials or older members of Generation X. The frequency of use of smartphone apps and the share of long-distance leisure trips made by plane affect the adoption of ridehailing for the members of this class. The members of this class also tend to adopt ridehailing if they live in neighborhoods with higher land-use mix and if they have used taxi services within the past 12 months. The lowest adoption rate is observed among the members of the class, comprising the least affluent individuals with the lowest level of education. The members of this class are more likely to live in rural neighborhoods and they rarely use ridehailing. The adoption of ridehailing among the members of this class is affected by household income, the frequency of long-distance non-car business trip, transit accessibility as well as the use of taxi and of carsharing.

The author estimated an ordered probit model with sample selection and a zero-inflated ordered probit model with correlated error terms to explore the impacts of various explanatory variables on the frequency of use of Uber/Lyft. The results show that 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 (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. 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.