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Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission

Author

Listed:
  • Bálint Kővári

    (Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary)

  • Lászlo Szőke

    (Robert Bosch Kft., H-1103 Budapest, Hungary)

  • Tamás Bécsi

    (Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary)

  • Szilárd Aradi

    (Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary)

  • Péter Gáspár

    (System and Control Lab, Institute for Computer Science and Control, H-1111 Budapest, Hungary)

Abstract

The traffic signal control problem is an extensively researched area providing different approaches, from classic methods to machine learning based ones. Different aspects can be considered to find an optima, from which this paper emphasises emission reduction. The core of our solution is a novel rewarding concept for deep reinforcement learning (DRL) which does not utilize any reward shaping, hence exposes new insights into the traffic signal control (TSC) problem. Despite the omission of the standard measures in the rewarding scheme, the proposed approach can outperform a modern actuated control method in classic performance measures such as waiting time and queue length. Moreover, the sustainability of the realized controls is also placed under investigation to evaluate their environmental impacts. Our results show that the proposed solution goes beyond the actuated control not just in the classic measures but in emission-related measures too.

Suggested Citation

  • Bálint Kővári & Lászlo Szőke & Tamás Bécsi & Szilárd Aradi & Péter Gáspár, 2021. "Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission," Sustainability, MDPI, vol. 13(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11254-:d:654567
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    References listed on IDEAS

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    1. Julian Schrittwieser & Ioannis Antonoglou & Thomas Hubert & Karen Simonyan & Laurent Sifre & Simon Schmitt & Arthur Guez & Edward Lockhart & Demis Hassabis & Thore Graepel & Timothy Lillicrap & David , 2020. "Mastering Atari, Go, chess and shogi by planning with a learned model," Nature, Nature, vol. 588(7839), pages 604-609, December.
    2. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Sorin Liviu Jurj & Tino Werner & Dominik Grundt & Willem Hagemann & Eike Möhlmann, 2022. "Towards Safe and Sustainable Autonomous Vehicles Using Environmentally-Friendly Criticality Metrics," Sustainability, MDPI, vol. 14(12), pages 1-52, June.
    2. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.

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