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.

The used car market is a critical element for the mass adoption of electric vehicles (EVs). However, most previous studies on EV adoption have focused only on new car markets. This article examines and compares the effects of charging infrastructure characteristics on the preferences for EVs among both new and used car buyers. This study is based on an online stated preference choice experiment among private car owners in the U.S., and the results of comparable binomial logistic models show that new and used car buyers generally share similar patterns in preferences for EVs, with exceptions for sensitivity toward fast charging time, and home charging solutions. Respondents’ stated willingness to adopt an EV increases considerably with improvements in driving range, and the effects on new and used car buyers are similar. The study also finds that better availability of charging infrastructure largely increases preference for EVs. The results further reveal that slow and fast charging have complementary effects on encouraging EV adoption as the combination of public slow and fast charging can compensate for the unavailability of home charging.

This study examined multitasking behaviors of drivers in environments that include large numbers of pedestrians and cyclists, using video and vehicle data from the second Strategic Highway Research Program (SHRP2). The study includes 15 sites in both Seattle, WA, and Tampa, FL, U.S., (nine pedestrian and six cyclist locations), including three marking and signal types for crosswalks and two types for bike treatments. A total of 1,458 SHRP2 traversals with time-series data and forward videos were extracted with face/dash videos for about 50% of these traversals. Forward video coding was conducted for all daytime traversals starting from one block before to one block after the selected site. Face/dash video was coded for all traversals with pedestrians or cyclists. A matched set of traversals without pedestrians or cyclists were also coded. The final data set included 458 traversals with coded data on multitasking behavior and the multimodal environment. Mixed-effect binary logistic regression models were used to examine the associations of pedestrian/cyclist presence and the facility type with drivers’ multitasking behavior. The findings show that the presence of pedestrians/cyclists and facility types could be related to drivers’ multitasking behavior. The findings can provide the foundation for future studies that examine safety for non-motorists with respect to infrastructure design, signage, and policies. There is also the potential to provide insights into assistive driving systems within automated vehicles, which are discussed in this paper.