Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers. (February 2020)

• Record Type: Journal Article
• Authors: Lei, Chao; Jiang, Zhoutong; Ouyang, Yanfeng
• Is Part Of: Transportation research. Volume 132(2020)
• Page Start: 60
• Page End: 75
• Publication Date: 2020-02
• Subjects: Ridesharing -- Dynamic pricing -- Self-driving vehicle -- Bi-level optimization -- MPEC -- Approximate dynamic programming; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2019.01.017
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: Dynamic path-based pricing strategy to enhance utilization of ridesharing resources. Embedded game model for decisions of the mobility service provider and travelers. Approximate dynamic programming approach to solve the multi-period model. Numerical examples show good performance of the proposed strategy. Abstract: 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.