Perceptions and preferences of autonomous and shared autonomous vehicles : a focus on dynamic ride-sharing

This thesis covers certain aspects of autonomous and shared autonomous vehicles (S/AVs), with a focus on dynamic ride-sharing (DRS). The first part investigates Americans’ preferences in adopting AVs. Rapid advances in technologies have accelerated the timeline for public use of fully-automated and communications-connected vehicles. Public opinion on self-driving vehicles or AVs is evolving rapidly, and many behavioral questions have not yet been addressed. This study emphasizes AV mode choices, including Americans’ willingness to pay (WTP) to ride with a stranger in a shared AV fleet vehicle on various trip types and the long-distance travel impacts of AVs. 2,588 complete responses to a stated-preference survey with 70 questions provide valuable insights on privacy concerns and crash ethics, safety and ride-sharing with strangers, long-distance travel and preferences for smarter vehicles and transport systems. While the starting sample data were relatively demographically unbiased, Texans were purposefully over-sampled, and all statistics adjusted/corrected (via sample weights) to match US demographics on gender, education, income, and age. Weighted results suggest that Americans are willing to pay, on average, $2073 to own AVs over conventional vehicles and an additional $1078 to maintain/include a manual driving option on such vehicles. Ride-sharing will be popular at 75¢ per mile, under most scenarios, and many Americans are willing to pay $1, on average, to anonymize their trip ends’ addresses. Most are also willing to let children 16 years of age and older have unsupervised access to AVs (both privately owned and shared). Nearly 50% of long-distance travel appears captured by AVs and SAVs in the future, rather than airlines, at least for one-way trip distances up to 500 miles. Two hurdle models (which allow for a high share of zero-value responses) were estimated: one to predict WTP to share a ride and another to determine WTP to anonymize location while using AVs. The first two-part model shows how travel time delays, person and household attributes, and land use densities can significantly affect Americans’ willingness to share rides. The second hurdle model suggests that traveler age, presence of children, household income, vehicle ownership and driver’s license status are major predictors of one’s WTP to obscure pick-up and drop-off locations. A binary logit was used to model current mode choice for long-distance (over 50 miles, one-way) travel (between one’s private car and an airplane), with household income as the leading predictor. On average, older Americans and/or those with children prefer such travel by car. Finally, a multinomial logit anticipated mode shifts when AVs and SAVs become available and affordable. Everything else constant, private cars remain preferred by older people, but SAVs may be used in the future for more business travel. In the second part of this thesis, a trip-matching framework is programmed to evaluate DRS opportunities for trips across Orlando, Florida. Transportation network companies (TNCs) are regularly demonstrating the economic and operational viability of DRS to any destination within a city, thanks to real-time information from smartphones. In the foreseeable future, fleets of SAVs may largely eliminate the need for human drivers, while lowering per-mile operating costs and increasing the convenience of travel. This may dramatically reduce private vehicle ownership and deliver extensive use of SAVs. Using AirSage’s cellphone-based trip tables across 1,267 zones over 30 consecutive days, this study anticipates DRS matches (by assigning independent travelers with overlapping routes in time and space to the same SAV) and simulates SAV travel across the Orlando network to determine optimal SAV fleet size. Those results suggest significant opportunities for DRS-enabled SAVs: Nearly 60% of the single-person trips can be shared with other persons traveling solo and with less than 5 minutes added travel time (to arrive at their destinations). This value climbs to 80% and 86% for 15 and 30 minutes of added wait or travel time, respectively. On the average travel day in Orlando, a fleet of just 30,000 SAVs can serve nearly 45% of those 3 million person-trips traveled solo. In other words, just 1 SAV per 100 person-trips is able to serve almost half of the region’s demand, helping reduce congestion while filling up passenger vehicle seats.