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 - Funding Information:
This research was supported in part by the U.S. National Science Foundation via Grants CMMI-1234085, CMMI-1662825, and a grant from the Zhejiang University - University of Illinois at Urbana-Champaign (UIUC) Institute Research Program. Very helpful comments from the three anonymous reviewers are also gratefully acknowledged.
Publisher Copyright:
© 2019 The Authors. Published by Elsevier B.V.
PY - 2018
Y1 - 2018
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.trpro.2019.05.006
DO - 10.1016/j.trpro.2019.05.006
M3 - Conference article
AN - SCOPUS:85074935499
SN - 2352-1457
VL - 38
SP - 77
EP - 97
JO - Transportation Research Procedia
JF - Transportation Research Procedia
T2 - 23rd International Symposium on Transportation and Traffic Theory, ISTTT 2019
Y2 - 24 July 2018 through 26 July 2018
ER -