A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

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 operators 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.

Bike sharing systems: a new incentive rebalancing method based on spatial outliers detection

“…Moreover, rebalancing is more expensive as bikes may be recovered independently. Furthermore, fleet dimensioning and location of the bikes appear as main issues as they can be fixed by optimization in Zhang et al [31] or deep-learning using data in Pan et al [32]. And this issue is also studied for docked BSS.…”

Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information

“…Singla et al [4] extend their work by incorporating a budget constraint on incentive provision and a learning procedure regarding a user utility function in optimal pricing into the model. [44] propose a deep reinforcement learning algorithm to solve a incentive-based rebalancing problem, which consider both spatial and temporal features in the system. Other related research includes [45][46][47][48][49][50].…”

Fleet Size and Rebalancing Analysis of Dockless Bike-Sharing Stations Based on Markov Chain

“…Ling Pan et al [26] believe that the rebalancing problem has a significant impact on the service quality and revenue of the dockless bike-sharing company. In order to solve the rebalancing problem,…”

“…The density of the city is negatively correlated with the bicycle idle time and is positively related to the rebalancing. The authors used the SPSS and ArcGIS software for statistical and spatial analysis.Ling Pan et al [26] believe that the rebalancing problem has a significant impact on the service quality and revenue of the dockless bike-sharing company. In order to solve the rebalancing problem,…”