Learning How to Price Charging in Electric Ride-Hailing Markets

TL;DR

This paper develops a learning-based pricing strategy for electric ride-hailing markets, modeling the interactions as a Stackelberg game and demonstrating its effectiveness through simulations in Shenzhen.

Contribution

It introduces a contextual bandit-based learning algorithm for central authorities to optimize charging prices in electric ride-hailing markets with multiple competing companies.

Findings

The algorithm effectively learns pricing strategies in simulated environments.

Partial knowledge of companies' objectives improves learning efficiency.

The approach is validated through a case study in Shenzhen.

Abstract

With the electrification of ride-hailing fleets, there will be a need to incentivize where and when the ride-hailing vehicles should charge. In this work, we assume that a central authority wants to control the distribution of the vehicles and can do so by selecting charging prices. Since there will likely be more than one ride-hailing company in the market, we model the problem as a single-leader multiple-follower Stackelberg game. The followers, i.e., the companies, compete about the charging resources under given prices provided by the leader. We present a learning algorithm based on the concept of contextual bandits that allows the central authority to find an efficient pricing strategy. We also show how the exploratory phase of the learning can be improved if the leader has some partial knowledge about the companies' objective functions. The efficiency of the proposed algorithm is demonstrated in a simulated case study for the city of Shenzhen, China.