IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5125724.html
   My bibliography  Save this article

Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems

Author

Listed:
  • Changle Sun
  • Hongyan Gao

Abstract

Foggy weather seriously deteriorates the performance of freeway systems, particularly regarding traffic safety and efficiency. General macroscopic traffic models have difficulty reflecting the characteristics of a freeway under foggy weather conditions. In the present study, a macroscopic traffic model using a correction factor under foggy weather conditions is therefore proposed, which is regulated according to the different levels of visibility and curve radius of the freeway using the Takagi–Sugeno (T-S) model. Based on the proposed traffic model, a local ramp metering strategy with density correction under foggy weather conditions is proposed to improve traffic safety. The proposed local ramp metering strategy regulates the on-ramp flow using the T-S model according to the mainstream density, speed, and visibility. The correction factors are determined based on the parameters of the consequent part in the T-S model, which are optimized using the particle swarm optimization algorithm. The sum of the mean absolute percentage error of the mainstream traffic density and speed is used to evaluate the proposed traffic model. The real-time crash-risk prediction model, which reflects the degree of traffic safety, is used to evaluate the proposed local ramp metering strategy. Simulations using VISSIM and MATLAB show that the proposed traffic model is suitable under foggy weather conditions and that the proposed local ramp metering strategy achieves a better performance in reducing fog-related crashes.

Suggested Citation

  • Changle Sun & Hongyan Gao, 2019. "Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems," Complexity, Hindawi, vol. 2019, pages 1-12, December.
    • Handle: RePEc:hin:complx:5125724
    DOI: 10.1155/2019/5125724
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/5125724.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2019/5125724.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/5125724?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Smaragdis, Emmanouil & Papageorgiou, Markos & Kosmatopoulos, Elias, 2004. "A flow-maximizing adaptive local ramp metering strategy," Transportation Research Part B: Methodological, Elsevier, vol. 38(3), pages 251-270, March.
    2. Chao Lu & Jie Huang, 2017. "A self-learning system for local ramp metering with queue management," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(2), pages 182-198, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shengnan Li & Hu Yang & Minglun Li & Jianjun Dai & Pu Wang, 2023. "A Highway On-Ramp Control Approach Integrating Percolation Bottleneck Analysis and Vehicle Source Identification," Sustainability, MDPI, vol. 15(16), pages 1-15, August.
    2. Pengsen Yang & Minghui Ma & Chaoteng Wu, 2024. "Ecologically Oriented Freeway Control Methods Integrated Speed Limits and Ramp Toll Booths Layout," Sustainability, MDPI, vol. 16(11), pages 1-14, May.
    3. Čičić, Mladen & Johansson, Karl Henrik, 2022. "Front-tracking transition system model for traffic state reconstruction, model learning, and control with application to stop-and-go wave dissipation," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 212-236.
    4. van Erp, Paul B.C. & Knoop, Victor L. & Hoogendoorn, Serge P., 2018. "Macroscopic traffic state estimation using relative flows from stationary and moving observers," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 281-299.
    5. Reilly, Jack & Martin, Sébastien & Payer, Mathias & Bayen, Alexandre M., 2016. "Creating complex congestion patterns via multi-objective optimal freeway traffic control with application to cyber-security," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 366-382.
    6. Michael L. Anderson & Lucas W. Davis, 2018. "Two Empirical Tests of Hypercongestion," NBER Working Papers 24469, National Bureau of Economic Research, Inc.
    7. Jovanović, Aleksandar & Kukić, Katarina & Stevanović, Aleksandar, 2021. "A fuzzy logic simulation model for controlling an oversaturated diverge diamond interchange and ramp metering system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 165-181.
    8. Zhouqiao Zhao & Guoyuan Wu & Matthew Barth, 2021. "Corridor-Wise Eco-Friendly Cooperative Ramp Management System for Connected and Automated Vehicles," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    9. Rudjanakanoknad, Jittichai, 2005. "Increasing Freeway Merge Capacity Through On-Ramp Metering," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3js9x18d, Institute of Transportation Studies, UC Berkeley.
    10. Anupriya & Daniel J. Graham & Daniel Horcher & Prateek Bansal, 2021. "Revisiting the empirical fundamental relationship of traffic flow for highways using a causal econometric approach," Papers 2104.02399, arXiv.org.
    11. Schmitt, Marius & Ramesh, Chithrupa & Lygeros, John, 2017. "Sufficient optimality conditions for distributed, non-predictive ramp metering in the monotonic cell transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 401-422.
    12. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:5125724. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.