Towards sustainability: decoding ridehailing drivers' and passengers' behaviors

Abstract

The goal of this dissertation is to understand and quantify the differences in behaviors between ride-hail drivers and ride-hail passengers. Ride-hailing refers to the organizations that provide on demand travel services (e.g., Uber, Lyft). The dissertation accomplishes this goal using two surveys. One that was distributed to Seattle, Washington ridehailing drivers (n=198) and another one that was distributed nationwide to ride-hail passengers (n=880). Two models were developed to examine the Seattle ridehailing drivers: a mixed discrete choice model was used to jointly model their working time choice and relocation choices, and a generalized additive mixed model (GAMM) was used to examine the drivers likelihood to accept a ride. For the nationwide survey on ridehailing passengers, an integrated choice and latent variable (ICLV) model was used to understand the passenger's choice between solo and shared rides. The research indicates that surge pricing significantly influences both the decisions of drivers regarding their working hours and their choices regarding relocation. Higher surge prices in a particular area tend to prolong the working hours of drivers there and attract drivers from other areas to relocate. Moreover, the study suggests that drivers tend to continue working when their earnings are high, while their predetermined working time target affects their decision to stop working more than their earnings target does. Regarding relocation decisions, drivers are inclined to remain in their current location, especially when trip requests are promptly received or when the relocation time is long. Concerning trip request acceptance, ride-hailing drivers display a reluctance to accept shared rides, typically requiring at least a $10 subsidy to do so. Similarly, passengers are hesitant to opt for shared rides, although their willingness increases with a greater level of trust in fellow passengers, a lower preference for private space, and a higher willingness to socialize during a ride. Gender does not significantly influence these tendencies. It is noted that the findings may be limited given the sampling scheme, but nonetheless, there are implications toward promoting shared rides in the future. Policy suggestions include differential taxation/fee structure for solo and shared rides, fostering public-private partnerships, refining matchmaking algorithms, and enhancing the overall experience for both drivers and passengers in shared rides.