A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

TL;DR

This paper introduces a deep reinforcement learning framework, HRP, for efficiently rebalancing dockless bike sharing systems by considering spatial and temporal factors, improving service quality and revenue.

Contribution

It presents a novel HRP algorithm that captures spatial-temporal dependencies for bike rebalancing, outperforming existing methods and generalizing well to new areas.

Findings

HRP achieves near 24-timeslot look-ahead optimization performance.

HRP outperforms state-of-the-art methods in service level and bike distribution.

HRP generalizes effectively to unseen areas.

Abstract

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas.