A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning

TL;DR

This paper introduces a distributed deep reinforcement learning algorithm for multi-hop ride-sharing that optimizes vehicle dispatch and matching, reducing costs and increasing fleet utilization.

Contribution

It presents a novel multi-hop ride-sharing algorithm using deep reinforcement learning, enabling vehicle transfers and improving efficiency over existing methods.

Findings

Achieves 30% lower operational costs.

Increases fleet utilization by 20%.

Enhances customer experience through multi-hop transfers.

Abstract

The growth of autonomous vehicles, ridesharing systems, and self driving technology will bring a shift in the way ride hailing platforms plan out their services. However, these advances in technology coupled with road congestion, environmental concerns, fuel usage, vehicles emissions, and the high cost of the vehicle usage have brought more attention to better utilize the use of vehicles and their capacities. In this paper, we propose a novel multi-hop ride-sharing (MHRS) algorithm that uses deep reinforcement learning to learn optimal vehicle dispatch and matching decisions by interacting with the external environment. By allowing customers to transfer between vehicles, i.e., ride with one vehicle for sometime and then transfer to another one, MHRS helps in attaining 30\% lower cost and 20\% more efficient utilization of fleets, as compared to the ride-sharing algorithms. This flexibility of multi-hop feature gives a seamless experience to customers and ride-sharing companies, and thus improves ride-sharing services.