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Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling
Abstract Shared automated vehicles (SAVs) have the potential to promote transit ridership by providing efficient first-mile last-mile (FMLM) connections through reduced operational costs to fleet providers and lower out-of-pocket costs to riders. To help plan for a future of integrated mobility, this paper investigates the impacts of SAVs serving FMLM connections, as a mode that provides flexibility in access/egress decisions and is well coordinated with train station schedules. To achieve this objective, a novel dynamic pooling algorithm was introduced to match SAVs with riders while coordinating the riders' arrival times at the light-rail station to a known train schedule. Microsimulations of SAVs and travelers throughout two central Austin neighborhoods show how larger service areas, higher levels of SAV demand, and longer arrival times between successive trains require larger SAV fleet sizes and higher SAV utilization rates to deliver close traveler wait times. Four-person SAVs appear to perform similar to 6-seat SAVs but will cost less to provide. Using a dynamic pooling algorithm tightly coordinated with train arrivals (every 15 min) delivers 87% of travelers to their stations in time to catch the next train, while uncoordinated assignments deliver just 58% of travelers in time.
Highlights This paper investigates the impacts of SAVs serving first-mile last-mile train connections in central Austin. 90% of the trips and 30% of travel distances are shared (among strangers) on board these SAVs. Four-seat SAVs perform similar to 6-seat SAVs but will cost less to provide. Reductions in seat capacity increases curb congestion (particularly with 1-seater SAVs). SAVs coordinated with train arrivals deliver almost 90% of travelers in time for the next train.
Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling
Abstract Shared automated vehicles (SAVs) have the potential to promote transit ridership by providing efficient first-mile last-mile (FMLM) connections through reduced operational costs to fleet providers and lower out-of-pocket costs to riders. To help plan for a future of integrated mobility, this paper investigates the impacts of SAVs serving FMLM connections, as a mode that provides flexibility in access/egress decisions and is well coordinated with train station schedules. To achieve this objective, a novel dynamic pooling algorithm was introduced to match SAVs with riders while coordinating the riders' arrival times at the light-rail station to a known train schedule. Microsimulations of SAVs and travelers throughout two central Austin neighborhoods show how larger service areas, higher levels of SAV demand, and longer arrival times between successive trains require larger SAV fleet sizes and higher SAV utilization rates to deliver close traveler wait times. Four-person SAVs appear to perform similar to 6-seat SAVs but will cost less to provide. Using a dynamic pooling algorithm tightly coordinated with train arrivals (every 15 min) delivers 87% of travelers to their stations in time to catch the next train, while uncoordinated assignments deliver just 58% of travelers in time.
Highlights This paper investigates the impacts of SAVs serving first-mile last-mile train connections in central Austin. 90% of the trips and 30% of travel distances are shared (among strangers) on board these SAVs. Four-seat SAVs perform similar to 6-seat SAVs but will cost less to provide. Reductions in seat capacity increases curb congestion (particularly with 1-seater SAVs). SAVs coordinated with train arrivals deliver almost 90% of travelers in time for the next train.
Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling
Huang, Yantao (author) / Kockelman, Kara M. (author) / Garikapati, Venu (author)
2021-10-20
Article (Journal)
Electronic Resource
English
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