Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets

TL;DR

This paper proposes a hierarchical game-based control mechanism for balancing the charging of ride-hailing electric fleets, considering multiple stakeholders and ensuring stable equilibria through theoretical and experimental analysis.

Contribution

It introduces a novel hierarchical pricing game framework that models interactions among ride-hailing fleets, external agents, and drivers, with proven equilibrium existence and robustness.

Findings

The mechanism achieves a unique Nash equilibrium in the upper-level game.

Robustness of the control mechanism is validated through theoretical analysis.

Case study demonstrates effective charging balance using real Shenzhen taxi data.

Abstract

Due to the ever-increasing popularity of ride-hailing services and the indisputable shift towards alternative fuel vehicles, the intersection of the ride-hailing market and smart electric mobility provides an opportunity to trade different services to achieve societal optimum. In this work, we present a hierarchical, game-based, control mechanism for balancing the simultaneous charging of multiple ride-hailing fleets. The mechanism takes into account sometimes conflicting interests of the ride-hailing drivers, the ride-hailing company management, and the external agents such as power-providing companies or city governments that will play a significant role in charging management in the future. The upper-level control considers charging price incentives and models the interactions between the external agents and ride-hailing companies as a Reverse Stackelberg game with a single leader and multiple followers. The lower-level control motivates the revenue-maximizing drivers to follow the company operator's requests through surge pricing and models the interactions as a single leader, multiple followers Stackelberg game. We provide a pricing mechanism that ensures the existence of a unique Nash equilibrium of the upper-level game that minimizes the external agent's objective at the same time. We provide theoretical and experimental robustness analysis of the upper-level control with respect to parameters whose values depend on sensitive information that might not be entirely accessible to the external agent. For the lower-level algorithm, we combine the Nash equilibrium of the upper-level game with a quadratic mixed integer optimization problem to find the optimal surge prices. Finally, we illustrate the performance of the control mechanism in a case study based on real taxi data from the city of Shenzhen in China.