Dynamic Pricing Mechanism Design for Electric Mobility-on-Demand Systems

With the popularization of ride-sharing transportation and increasing penetration of electric vehicles (EVs) in recent years, the electric mobility-on-demand (EMoD) system is emerging as a promising means to provide ride-sharing services in the context of sustainable cities. In this paper, we focus on sequential decision-making for the operator of an EMoD system by considering both the passengers' utility and system revenue. Specifically, we design a pricing mechanism to incentivize passengers with spatially and temporally different demand to make different mobility choices. After the passengers' demand is realized, the operator makes operational decisions, including dispatching and repositioning EVs between service regions, and recharging EVs to maintain their energy levels. Therefore, a bi-level and dynamic programming problem is formulated to model these decisions. To solve this problem, we first transform the bi-level problem into a single-level one based on the structural properties of the formulation. Furthermore, we rigorously prove the coordinate-wise concavity of the single-level formulation and efficiently obtain near-optimal solutions based on approximation. Numerical tests show that the proposed dynamic pricing mechanism achieves a significantly better performance than static pricing and other existing model-free approaches (e.g., Q-learning).