TY - GEN
T1 - PoolLines
T2 - 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022
AU - Chaabouni, Youssef
AU - Araldo, Andrea
AU - De Palma, André
AU - Arib, Souhila
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - In carpooling systems, a set of drivers owning a private car can accept a small detour to pick-up and drop-off other riders. However, carpooling is widely used for long-distance trips, where rider-driver matching can be done days ahead. Making carpooling a viable option for daily commute is more challenging, as trips are shorter and, proportionally, the detours tolerated by drivers are more tight. As a consequence, finding riders and drivers sharing close-enough origins, destinations and departure time is less likely, which limits potential matching. In this paper we propose an Integrated System, where carpooling matching is synchronized with Public Transit (PT) schedules, so as to serve as a feeder service to PT in the first mile. Driver detours are proposed towards PT selected stations, which are used as consolidation points, thus increasing matching probability. We present a computationally efficient method to represent PT schedules and drivers trajectory in a single General Transit Feed Specification database, which allows to compute multimodal rider journeys using any off the shelf planners. We showcase our approach in the metropolitan area of Portland, Oregon, considering 8k randomly generated trips. We show the benefits of our Integrated System. We find that 10% more riders find a feasible matching with respect to the status quo, where carpooling and PT are operated separately. We release our code as open source.1
AB - In carpooling systems, a set of drivers owning a private car can accept a small detour to pick-up and drop-off other riders. However, carpooling is widely used for long-distance trips, where rider-driver matching can be done days ahead. Making carpooling a viable option for daily commute is more challenging, as trips are shorter and, proportionally, the detours tolerated by drivers are more tight. As a consequence, finding riders and drivers sharing close-enough origins, destinations and departure time is less likely, which limits potential matching. In this paper we propose an Integrated System, where carpooling matching is synchronized with Public Transit (PT) schedules, so as to serve as a feeder service to PT in the first mile. Driver detours are proposed towards PT selected stations, which are used as consolidation points, thus increasing matching probability. We present a computationally efficient method to represent PT schedules and drivers trajectory in a single General Transit Feed Specification database, which allows to compute multimodal rider journeys using any off the shelf planners. We showcase our approach in the metropolitan area of Portland, Oregon, considering 8k randomly generated trips. We show the benefits of our Integrated System. We find that 10% more riders find a feasible matching with respect to the status quo, where carpooling and PT are operated separately. We release our code as open source.1
KW - GTFS
KW - carpooling
KW - open data
KW - public transit
KW - transportation
UR - https://www.scopus.com/pages/publications/85142520374
U2 - 10.1145/3557991.3567795
DO - 10.1145/3557991.3567795
M3 - Conference contribution
AN - SCOPUS:85142520374
T3 - Proceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022
SP - 60
EP - 63
BT - Proceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022
A2 - Berres, Andy
A2 - Kurte, Kuldeep
A2 - Xu, Haowen
PB - Association for Computing Machinery, Inc
Y2 - 1 November 2022
ER -