TY - JOUR
T1 - A framework for railway transit network design with first-mile shared autonomous vehicles
AU - Shan, Ali
AU - Hoang, Nam Hong
AU - An, Kun
AU - Vu, Hai L.
N1 - Funding Information:
This research was supported by the Australian Research Council (ARC) Discovery Grant DP180102551 .
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/9
Y1 - 2021/9
N2 - Providing a railway transit system (RTS) in less populated areas is a challenging task for transportation agencies due to its high construction and operating costs. With the advent of automation, shared autonomous vehicles (SAVs) as an integral part of public transit services has the potential to enhance the design of transit systems. In this paper, we present a joint optimization framework of railway transit network design and SAV first-mile service that minimizes the total cost of the combined RTS-SAV services and commuters’ waiting time, while serving a dynamic travel demand in the network. The proposed model optimizes the SAV fleet size and the RTS alignment while enabling vehicle relocations to tackle the vehicle imbalance issue in the SAV service. Due to the non-linear and mixed-integer formulation, we develop a fixed-point algorithm for this joint RTS-SAV problem where we transform the original problem into a mixed-integer linear programming (MILP) formulation. Our results indicate that the joint RTS-SAV services can be constructed and operated at a lower cost than either of the RTS or SAV services alone. Furthermore, the resulting joint RTS-SAV services are underpinned by a shorter railway alignment and larger fleet size rather than a multi-link extension. Additionally, the joint RTS-SAV services is robust to the variation in total demand, with respect to the railway alignment, SAV utilization and commuters’ waiting time.
AB - Providing a railway transit system (RTS) in less populated areas is a challenging task for transportation agencies due to its high construction and operating costs. With the advent of automation, shared autonomous vehicles (SAVs) as an integral part of public transit services has the potential to enhance the design of transit systems. In this paper, we present a joint optimization framework of railway transit network design and SAV first-mile service that minimizes the total cost of the combined RTS-SAV services and commuters’ waiting time, while serving a dynamic travel demand in the network. The proposed model optimizes the SAV fleet size and the RTS alignment while enabling vehicle relocations to tackle the vehicle imbalance issue in the SAV service. Due to the non-linear and mixed-integer formulation, we develop a fixed-point algorithm for this joint RTS-SAV problem where we transform the original problem into a mixed-integer linear programming (MILP) formulation. Our results indicate that the joint RTS-SAV services can be constructed and operated at a lower cost than either of the RTS or SAV services alone. Furthermore, the resulting joint RTS-SAV services are underpinned by a shorter railway alignment and larger fleet size rather than a multi-link extension. Additionally, the joint RTS-SAV services is robust to the variation in total demand, with respect to the railway alignment, SAV utilization and commuters’ waiting time.
KW - Dynamic travel demand
KW - Fixed-point method
KW - Mixed-integer programming
KW - Railway transit network design
KW - Shared autonomous vehicles
KW - Vehicle relocations
UR - http://www.scopus.com/inward/record.url?scp=85110452085&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2021.103223
DO - 10.1016/j.trc.2021.103223
M3 - Article
AN - SCOPUS:85110452085
SN - 0968-090X
VL - 130
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
M1 - 103223
ER -