A Cost-Aware Adaptive Bike Repositioning Agent Using Deep Reinforcement Learning

Advanced search
Browse by:

Department | Year

UCL Theses | Latest

Deposit your research

A Cost-Aware Adaptive Bike Repositioning Agent Using Deep Reinforcement Learning

Staffolani, Alessandro; Darvariu, Victor-Alexandru; Bellavista, Paolo; Musolesi, Mirco; (2025) A Cost-Aware Adaptive Bike Repositioning Agent Using Deep Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems 10.1109/tits.2025.3535915. (In press). Green open access

Preview

PDF
A_Cost-Aware_Adaptive_Bike_Repositioning_Agent_Using_Deep_Reinforcement_Learning.pdf - Accepted Version
Download (20MB) | Preview

Abstract

Bike Sharing Systems (BSS) represent a sustainable and efficient urban transportation solution. A major challenge in BSS is repositioning bikes to avoid shortage events when users encounter empty or full bike lockers. Existing algorithms unrealistically rely on precise demand forecasts and tend to overlook substantial operational costs associated with reallocations. This paper introduces a novel Cost-aware Adaptive Bike Repositioning Agent (CABRA), which harnesses advanced deep reinforcement learning techniques in dock-based BSS. By analyzing demand patterns, CABRA learns adaptive repositioning strategies aimed at reducing shortages and enhancing truck route planning efficiency, significantly lowering operational costs. We perform an extensive experimental evaluation of CABRA utilizing real-world data from Dublin, London, Paris, and New York. The reported results show that CABRA achieves operational efficiency that outperforms or matches very challenging baselines, obtaining a significant cost reduction. Its performance on the largest city comprising 1765 docking stations highlights the efficiency and scalability of the proposed solution even when applied to BSS with a great number of docking stations.

Type:	Article
Title:	A Cost-Aware Adaptive Bike Repositioning Agent Using Deep Reinforcement Learning
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1109/tits.2025.3535915
Publisher version:	https://doi.org/10.1109/tits.2025.3535915
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Costs, Vehicle dynamics, Urban areas, Docking stations, Deep reinforcement learning, Scalability, Optimization, Electronic mail, Computer science, Shared transport
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10204707