Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach

TL;DR

This paper introduces a multi-agent hierarchical reinforcement learning framework for efficient vehicle dispatching and routing in intercity ride-pooling, improving system profit and order fulfillment.

Contribution

It presents a novel multi-agent feudal reinforcement learning model combined with adaptive routing heuristics for real-time fleet management in intercity ride-pooling.

Findings

Significant increase in system profit and order fulfillment ratio.

Effective mitigation of supply and demand imbalances.

Validated on realistic dataset from Chinese cities.

Abstract

The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio.