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Volume 11 Issue 10
Oct.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(10): 2043-2058
Q. Ji, X. Wen, J. Jin, Y. Zhu, and  Y. Lv,  “Urban traffic control meets decision recommendation system: A survey and perspective,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 10, pp. 2043–2058, Oct. 2024. doi: 10.1109/JAS.2024.124659
Citation: Q. Ji, X. Wen, J. Jin, Y. Zhu, and  Y. Lv,  “Urban traffic control meets decision recommendation system: A survey and perspective,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 10, pp. 2043–2058, Oct. 2024. doi: 10.1109/JAS.2024.124659
shu

Urban Traffic Control Meets Decision Recommendation System: A Survey and Perspective

doi: 10.1109/JAS.2024.124659
Funds:  This work was supported by the National Key Research and Development Program of China (2021YFB2900200), the Key Research and Development Program of Science and Technology Department of Zhejiang Province (2022C01121), and Zhejiang Provincial Department of Transport Research Project (ZJXL-JTT-202223)
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    Abstract

  • Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems. Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level, utilizing their knowledge and expertise. However, this process is cumbersome, labor-intensive, and cannot be applied on a large network scale. Recent studies have begun to explore the applicability of recommendation system for urban traffic control, which offer increased control efficiency and scalability. Such a decision recommendation system is complex, with various interdependent components, but a systematic literature review has not yet been conducted. In this work, we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control, demonstrates the utility and efficacy of such a system in the real world using data and knowledge-driven approaches, and discusses the current challenges and potential future directions of this field.

     

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    Highlights

    • Presents a survey on decision recommendation systems in traffic management
    • Illustrates key components in traffic control decision recommendation systems
    • Highlights the use of human- and data-driven methods for traffic optimization
    • Discusses challenges and future directions in intelligent urban traffic control

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