Publication Detail

Exploring the Relationships between the Use of Uber and Lyft and Other Components of Travel Behavior in California

UCD-ITS-RP-19-19

Reprint

Available online at: https://trid.trb.org/view/1572804

Suggested Citation:
Alemi, Farzad and Giovanni Circella (2019) Exploring the Relationships between the Use of Uber and Lyft and Other Components of Travel Behavior in California. Transportation Research Board 98th Annual Meeting

In this paper, the authors investigate the relationships among the use of ridehailing (e.g. Uber and Lyft), the use of other means of transportation and the average weekly vehicle miles driven (VMD), using the California Millennials Dataset, a rich behavioral dataset collected in fall 2015 with an online survey administered to almost 2,000 young adults (millennials) and members of the preceding Generation X. The authors estimate a latent-class model with a continuous distal outcome to classify individuals based on their travel patterns and to investigate the variation in VMD across classes of travelers. Three latent classes are identified: (1) Multimodal Drivers, a class that is largely composed of more highly-educated, independent millennials, who have the highest average frequency of use of ridehailing. The members of this class use a variety of transportation modes, although driving is still their primary mode. They have the second highest average weekly VMD. (2) The lowest frequency of use of ridehailing is observed among Drivers, a class that is mainly composed of affluent Gen Xers living in suburban/rural settings. The members of this class tend to drive almost all the time for both commute and non-commute trips, and have the highest average VMD. (3) The smallest class includes the Multimodal No Car users, who tend to use public transit, walk or bike for both commute and non-commute trips, and have the second highest frequency of use of ridehailing. Less affluent dependent millennials are more likely to belong to this class, which has the lowest average VMD.

Key words: Uber, Lyft, ridehailing, latent class model with distal outcome, modality style, vehicle miles driven.