This study explores the factors that affect the use of ridehailing services (Uber, Lyft) as well as the adoption of shared (pooled) ridehailing (UberPool, Lyft Share) using data collected in California in fall 2018 using a cross-sectional travel survey. A semi-ordered bivariate probit model is estimated using this dataset. Among other findings, the model results show that better-educated, younger individuals who currently work or work and study are more likely to use shared ridehailing services than other individuals, and in particular members of older cohorts. Being white and living in a higher-income household is associated with a higher likelihood of being a frequent user of regular ridehailing but does not have statistically significant effects on the likelihood of adopting shared ridehailing. With respect to the factors limiting the use of shared ridehailing services, it was found that the increased travel time and lack of privacy discourage the adoption of shared ridehailing. Evidence is also found that some land-use features affect the likelihood of using both types of services. While the likelihood of using both ridehailing and shared ridehailing is higher in urban areas, residents of neighborhoods with higher intersection density are found to be more likely to adopt shared ridehailing only. However, some of the land-use variables become insignificant after introducing individuals’ attitudes related to land use into the model. This is an indication of residential self-selection, and the potential risk of attributing impacts to land-use features if individual attitudes are not explicitly controlled for.

Key words: ridehailing, consumer behavior, demographics