A STUDY OF NEW YORKERS’ PREFERENCE FOR AUTONOMOUS VEHICLES IN NYC BASED ON SURVEY DATA APPLYING DISCRETE CHOICE MODEL METHODS

75 pagesWith continuous growth of urban populations, transportation system faces numerouschallenges such as surging demand of real time services, insufficient government investment and sustainability of environment. Shared autonomous vehicles might be a good way to tackle these problems. SAVs could provide relatively cheap mobility on-demand services and new technology like electrical autonomous vehicles have gained wide popularity across the world as it’s a more environmental-friendly and energy efficient way of travel. As prosperous as this market seems, it becomes critical for us to study passengers’ attitudes and preference towards SAVs, since it not only enriches behavioral study for suppliers in this market but also helps suppliers to design more reasonable operation strategies based on the study. This article intends to advance future research about the travel behavior impacts of SAVs, by identifying the characteristics of users who are likely to adopt SAV services and establishing willingness to pay measures for service attributes. This research uses the stated preference survey data conducted by Professor Ricardo using Qualtrics, applying a conditional logit model to study factors influencing the preferences and then a mixed logit model to study the unobserved heterogeneity in the distributions of travelers’ preference. The results show that service attributes including travel cost, travel time and waiting time are critical determinants of the use of SAVs and the acceptance of DRS. Differences in willingness to pay for service attributes indicate that SAVs with DRS and SAVs without DRS are perceived as two distinct mobility options. The results imply that the adoption of SAVs may differ across subgroups, whereby young individuals and individuals with multimodal travel patterns are more likely to adopt SAVs. The methodological limitations of the study are also acknowledged. Despite a potential hypothetical bias, the results capture the directionality and relative importance of the attributes of interest