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Developing a fuzzy-based decision-making procedure for traffic control in expressway congestion management

Author

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  • Toan, Trinh Dinh
  • Wong, Yiik Diew
  • Lam, Soi Hoi
  • Meng, Meng

Abstract

This paper presents part of a multi-stage fuzzy logic controller (MS-FLC) that is developed for traffic control in congestion management on expressways. The decision-making process of traffic control for expressway congestion management using the MS-FLC consists of three tasks: (1) evaluation of current traffic congestion; (2) prediction of traffic congestion tendency; and (3) recommendation of control strategies and control actions to alleviate the congestion. This paper presents the 3rd stage of the MS-FLC that develops a fuzzy-based decision-making procedure (FDMP) for management of recurring and non-recurring congestion. Using fuzzy rules, the FDMP evaluates the current and anticipated traffic data and incident information to recommend control strategies at the strategic level, and control actions at the operational level. Results from this research show that: (i) the FDMP offers a comprehensive procedure in deriving control strategies and actions; (ii) FDMP control actions are derived from a systematic decision-making logic where the design of control rules is consistently oriented toward achieving desirable control objectives; (iii) the FDMP targets a proper balance in congestion management between the mainline and the ramp using compromise rule design; (iv) the FDMP facilitates using various forms of available traffic and incident data on an extended expressway segment to derive at control actions, making the system-wide gains possible; and (v) the FDMP could be applied for management of both recurring and non-recurring congestion.

Suggested Citation

  • Toan, Trinh Dinh & Wong, Yiik Diew & Lam, Soi Hoi & Meng, Meng, 2022. "Developing a fuzzy-based decision-making procedure for traffic control in expressway congestion management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122005763
    DOI: 10.1016/j.physa.2022.127899
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    References listed on IDEAS

    as
    1. Liu, Qingchao & Liu, Tao & Cai, Yingfeng & Xiong, Xiaoxia & Jiang, Haobin & Wang, Hai & Hu, Ziniu, 2021. "Explanatory prediction of traffic congestion propagation mode: A self-attention based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    2. Jin, Wenlong & Zhang, Michael, 2001. "Evaluation of On-ramp Control Algorithms," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1gz7w0wm, Institute of Transportation Studies, UC Berkeley.
    3. Toan, Trinh Dinh & Lam, Soi Hoi & Wong, Yiik Diew & Meng, Meng, 2022. "Development and validation of a driving simulator for traffic control using field data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    4. Zhang, Michael & Kim, Taewan & Nie, Xiaojian & Jin, Wenlong & Chu, Lianyu & Recker, Will, 2001. "Evaluation of On-ramp Control Algorithms," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt83n4g2rq, Institute of Transportation Studies, UC Berkeley.
    5. Sheu, Jiuh-Biing, 2007. "Stochastic modeling of the dynamics of incident-induced lane traffic states for incident-responsive local ramp control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 365-380.
    6. Toan, Trinh Dinh & Wong, Y.D., 2021. "Fuzzy logic-based methodology for quantification of traffic congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
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