App-based ridehailing services such as Uber and Lyft have revolutionized urban travel. These services improve mobility and reduce demand for parking, but also increase vehicle travel and shift some trips away from walking and public transit.1 As a result, ridehailing has been the largest contributor to increased congestion in recent years in cities such as San Francisco.2 Ridehil services could also be contributing to traffic congestion and pollution when vehicles are out of service between paid rides. Drivers might cruise (circle around while waiting for the next paid ride) or reposition (move to another location in anticipation of the next ride request), both of which can exacerbate congestion and pollution. They might also park (either on- or off-street), which would reduce congestion and pollution but may affect parking and curbspace availability or interfere with other street activities such as drop-offs and deliveries. To gain a better understanding of ridehail driver behavior between paid rides, UC researchers evaluated over 5.3 million ridehail trips in San Francisco in November and December 2016. Each trip was divided into cruising, repositioning, and parking segments.

We analyze what ridehail drivers do when out of service between paid trips. We use a dataset of 5.3 million trips in San Francisco and partition each out of service trip into cruising, repositioning, and parking segments. We find that repositioning accounts for nearly two-thirds (63%) of the time between trips, with cruising and parking accounting for 23% and 14% respectively (these figures exclude short trips). Our regression models suggest that drivers tend to make reasonable choices between repositioning and parking, heading to high-demand locations based on the time of day. However, we also find suggestive evidence of racial bias, supporting previous studies of both taxis and ridehailing that indicate that drivers tend to avoid neighborhoods with high proportions of residents of color.

AbstractWe analyze what ridehail drivers do when searching for paid fares. We use a dataset of 5.3 million trips in San Francisco and partition each search trip into cruising, repositioning, and parking segments. We find that repositioning accounts for nearly two-thirds (63%) of the time between trips, with cruising and parking accounting for 23% and 14% respectively (these figures exclude short trips). Our regression models suggest that drivers tend to make reasonable choices between repositioning and parking, heading to high-demand locations based on the time of day. However, we also find evidence of racial disparities, supporting previous studies of both taxis and ridehailing that indicate that drivers tend to avoid neighborhoods with high proportions of residents of color.