TY - GEN
T1 - Usage Patterns and Perceptions of Shared Autonomous Vehicles (SAVs)
T2 - 18th International Conference on Automated People Movers and Automated Transit Systems, APM-ATS 2022
AU - Arif Khan, Muhammad
AU - Etminani-Ghasrodashti, Roya
AU - Kermanshachi, Sharareh
AU - Michael Rosenberger, Jay
AU - Foss, Ann
N1 - Publisher Copyright:
© 2022 ASCE.
PY - 2022
Y1 - 2022
N2 - Technology-driven mobility services such as shared autonomous vehicles (SAVs) are gaining in popularity, and many cities in the world are implementing pilot projects that provide flexible on-demand services. SAVs strengthen the accessibility and mobility of underserved populations and enhance the efficiency of existing transit services by offering first/last mile connectivity. Due to a lack of empirical data, most of the existing literature on SAVs is based on hypothetical scenarios and simulation models; literature on modeling actual data is still scant. This study, developed based on real-time ridership data from an SAV service platform, is intended to fill that gap. We use the cluster analysis technique to identify patterns in trips lengths and durations, trip purposes, and bookings and the results indicated differences in the segments of users. The findings of this study will be helpful in planning, implementation, and marketing future SAV projects.
AB - Technology-driven mobility services such as shared autonomous vehicles (SAVs) are gaining in popularity, and many cities in the world are implementing pilot projects that provide flexible on-demand services. SAVs strengthen the accessibility and mobility of underserved populations and enhance the efficiency of existing transit services by offering first/last mile connectivity. Due to a lack of empirical data, most of the existing literature on SAVs is based on hypothetical scenarios and simulation models; literature on modeling actual data is still scant. This study, developed based on real-time ridership data from an SAV service platform, is intended to fill that gap. We use the cluster analysis technique to identify patterns in trips lengths and durations, trip purposes, and bookings and the results indicated differences in the segments of users. The findings of this study will be helpful in planning, implementation, and marketing future SAV projects.
UR - http://www.scopus.com/inward/record.url?scp=85138817518&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138817518&partnerID=8YFLogxK
U2 - 10.1061/9780784484388.009
DO - 10.1061/9780784484388.009
M3 - Conference contribution
AN - SCOPUS:85138817518
T3 - Automated People Movers and Automated Transit Systems 2022 - Proceedings of the 18th International Conference on Automated People Movers and Automated Transit Systems
SP - 94
EP - 104
BT - Automated People Movers and Automated Transit Systems 2022 - Proceedings of the 18th International Conference on Automated People Movers and Automated Transit Systems
A2 - Sproule, William J.
PB - American Society of Civil Engineers (ASCE)
Y2 - 31 May 2022 through 3 June 2022
ER -