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Cited by 149Transportation and Mobility InnovationsTransportation Planning and OptimizationTraffic Prediction and Management Techniques

Rebalancing shared mobility-on-demand systems: A reinforcement learning approach

Wen, Jian, Zhao, Jinhua, Jaillet, Patrick·2017·2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
DOI 10.1109/itsc.2017.8317908
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Abstract

Shared mobility-on-demand systems have very promising prospects in making urban transportation efficient and affordable. However, due to operational challenges among others, many mobility applications still remain niche products. This paper addresses rebalancing needs that are critical for effective fleet management in order to offset the inevitable imbalance of vehicle supply and travel demand. Specifically, we propose a reinforcement learning approach which adopts a deep Q network and adaptively moves idle vehicles to regain balance. This innovative model-free approach takes a very different perspective from the state-of-the-art network-based methods and is able to cope with large-scale shared systems in real time with partial or full data availability. We apply this approach to an agent-based simulator and test it on a London case study. Results show that, the proposed method outperforms the local anticipatory method by reducing the fleet size by 14% while inducing little extra vehicle distance traveled. The performance is close to the optimal solution yet the computational speed is 2.5 times faster. Collectively, the paper concludes that the proposed rebalancing approach is effective under various demand scenarios and will benefit both travelers and operators if implemented in shared mobility-on-demand systems.

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Crossref · 10.1109/itsc.2017.8317908

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23 of 23 references verified0 citation errors · 0 not checkable

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  • Shared mobility is disrupting travel and vehicle ownership, and ML plays an integral part in running and optimizing these services.

    “In the passenger sector, shared mobility (such as on-demand ride services or vehicle-sharing 14 ), is undoubtedly disrupting the way people travel and think about vehicle ownership, and ML plays an integral part in running and optimizing these services (e.g., [768,837]).”

    From Tackling climate change with machine learning (2023) · cites this paper as [837]

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Related

Authors
Wen, J., Zhao, J., & Jaillet, P.
Affiliations
Massachusetts Institute of Technology (US)
ORCID
Venue
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
Publisher
IEEE
Published
2017-10
Pages
220-225
DOI
10.1109/itsc.2017.8317908
OpenAlex
W2790769276
Cited by
149
References
23
Citations by year
2026: 2 · 2025: 15 · 2024: 20 · 2023: 21 · 2022: 28 · 2021: 30 · 2020: 22 · 2019: 8 · 2018: 3
FWCI
10.02
Type
conference paper
Language
en
Open access
closed
Locations
IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
Keywords
vehicle rebalancing, fleet management, deep Q-network, reinforcement learning, agent-based modeling, real-time decision making

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