TY - JOUR
T1 - Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers
AU - Lei, Chao
AU - Jiang, Zhoutong
AU - Ouyang, Yanfeng
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2020/2
Y1 - 2020/2
N2 - Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand.
AB - Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand.
KW - Approximate dynamic programming
KW - Bi-level optimization
KW - Dynamic pricing
KW - MPEC
KW - Ridesharing
KW - Self-driving vehicle
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U2 - 10.1016/j.trb.2019.01.017
DO - 10.1016/j.trb.2019.01.017
M3 - Article
AN - SCOPUS:85061573665
SN - 0191-2615
VL - 132
SP - 60
EP - 75
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
ER -