Analyzing the Effect of Autonomous Ridehailing on Transit Ridership: Competitor or Desirable First-/Last-Mile Connection?

Ridehailing services (e.g., Uber or Lyft) may serve as a substitute or a complement—or some combination thereof—to transit. Automation as an emerging technology is expected to further complicate the current complex relationship between transit and ridehailing. This paper aims to explore how US commuters’ stated willingness to ride transit is influenced by the price of ridehailing services and whether the service is provided by an autonomous vehicle. To that end, a stated preference survey was launched around the US to ask 1,500 commuters how they would choose their commute mode from among choices including their current mode and other conventional modes as well as asking them to choose between their current mode and an autonomous mode. Using a joint stated and revealed preference dataset, a mixed logit model was developed and analyzed. The results show that ridehailing per se might not be a significant competitor to transit, especially if it is integrated with transit as a first-/last-mile service. The total share of transit (transit-only riders plus those who use transit in connection with first-/last-mile ridehailing) remains substantially flat as set against conventional ridehailing services, even if ridehailing fares decrease. On the other hand, when the ridehailing price is significantly reduced by automation, our analysis suggests a decline in total transit ridership and an increase in ridehailing, especially for solo ridehailing. Also, it was found that autonomous pooled ridehailing might not be as appealing to commuters as autonomous solo ridehailing

Curbing cruising-as-substitution-for-parking in automated mobility

This study considers dynamic parking management in the era of driving automation that users of privately-owned automated vehicles (AVs) come to a downtown area to conduct various activities and choose between three parking options: outside parking, on-street parking, and cruising-as-substitution-for-parking. In the latter, AVs cruise in downtown until summoned by their users after concluding their activities. Given the distribution of users’ activity time in downtown, we propose a system of ordinary differential equations to model AVs’ parking choice and capture the impact of cruising-as-substitution-for-parking on traffic congestion. With the proposed model, we further investigate dynamic time-based tolling and parking provision to optimize the system performance. Results of our numerical experiments demonstrate the validity of our model and the potential of dynamic tolling and parking provision on managing downtown parking of AVs