A review on reinforcement learning methods for mobility on demand systems

TL;DR

This paper reviews reinforcement learning methods for mobility on demand systems, focusing on algorithmic details, use cases, and validation methods to advance the state of the art in operational strategies.

Contribution

It provides a unified classification framework for RL algorithms in MoD systems, analyzes their application contexts, and discusses validation approaches, highlighting research directions.

Findings

Classification of RL algorithms in MoD systems

Analysis of use case features and testing scenarios

Discussion of validation methods across studies

Abstract

Mobility on Demand (MoD) refers to mobility systems that operate on the basis of immediate travel demand. Typically, such a system consists of a fleet of vehicles that can be booked by customers when needed. The operation of these services consists of two main tasks: deciding how vehicles are assigned to requests (vehicle assignment); and deciding where vehicles move (including charging stations) when they are not serving a request (rebalancing). A field of research is emerging around the design of operation strategies for MoD services, and an increasingly popular trend is the use of learning based (most often Reinforcement Learning) approaches. We review, in this work, the literature on algorithms for operation strategies of MoD systems that use approaches based on Reinforcement Learning with a focus on the types of algorithms being used. The novelty of our review stands in three aspects: First, the algorithmic details are discussed and the approaches classified in a unified framework for sequential decision-making. Second, the use cases on which approaches are tested and their features are taken into account. Finally, validation methods that can be found across the literature are discussed. The review aims at advancing the state of the art by identifying similarities and differences between approaches and highlighting current research directions.