IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i3p830-d203739.html
   My bibliography  Save this article

Assessing the Influence of Adverse Weather on Traffic Flow Characteristics Using a Driving Simulator and VISSIM

Author

Listed:
  • Chen Chen

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Xiaohua Zhao

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Hao Liu

    (Beijing Transportation Information, Beijing 100161, China)

  • Guichao Ren

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Yunlong Zhang

    (Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA)

  • Xiaoming Liu

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

Abstract

The occurrence of adverse weather exacerbates traffic flow conditions, often leading to severe traffic congestions. Many studies have been conducted based on field-collected data to obtain the effects of weather on traffic flow characteristics. However, there is a limitation for filed data-based studies, in that weather conditions and traffic conditions are both noncontrollable and nonrepeatable, making it difficult to comprehensively assess the influence of weather conditions, especially the rare extreme weather conditions, on traffic flow characteristics. This paper proposes to assess these effects with the combination of driving simulator and traffic simulation. A driving simulator can collect driving behavior by conducting weather-related driving simulation experiments, while a microscopic traffic simulation program can evaluate the changes in traffic flow characteristics by inputting driving behavior parameters coming from the driving simulator. The proposed method can overcome the limitation of the field data-based approach. In this paper, the structure of the assessment platform is introduced at first. Then a verification experiment is conducted to measure the influences of adverse weather conditions on traffic flow characteristics. The verification experiment results show that the influences of adverse weather on traffic flow characteristics have consistent tendencies with outcomes from previous research and demonstrate that the method is practicable for the analysis of the influence of weather on traffic flow characteristics. This paper provides a practical way to analyze the influence of weather on traffic flow from driving behavior’s point of view.

Suggested Citation

  • Chen Chen & Xiaohua Zhao & Hao Liu & Guichao Ren & Yunlong Zhang & Xiaoming Liu, 2019. "Assessing the Influence of Adverse Weather on Traffic Flow Characteristics Using a Driving Simulator and VISSIM," Sustainability, MDPI, vol. 11(3), pages 1-16, February.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:830-:d:203739
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/3/830/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/3/830/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaohua Zhao & Wei Guan & Xiaoming Liu, 2013. "A Pilot Study Verifying How the Curve Information Impacts on the Driver Performance with Cognition Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-8, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hassan M. Al-Ahmadi & Arshad Jamal & Imran Reza & Khaled J. Assi & Syed Anees Ahmed, 2019. "Using Microscopic Simulation-Based Analysis to Model Driving Behavior: A Case Study of Khobar-Dammam in Saudi Arabia," Sustainability, MDPI, vol. 11(11), pages 1-18, May.
    2. Marijo Vidas & Vladan Tubić & Ivan Ivanović & Marko Subotić, 2022. "One Approach to Quantifying Rainfall Impact on the Traffic Flow of a Specific Freeway Segment," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    3. Bawan Mahmood & Jalil Kianfar, 2019. "Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    4. Jacek Oskarbski & Konrad Biszko, 2022. "Estimation of Vehicle Energy Consumption at Intersections Using Microscopic Traffic Models," Energies, MDPI, vol. 16(1), pages 1-35, December.
    5. Maksymilian Mądziel, 2023. "Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones," Energies, MDPI, vol. 16(19), pages 1-16, October.
    6. Changyin Dong & Hao Wang & Quan Chen & Daiheng Ni & Ye Li, 2019. "Simulation-Based Assessment of Multilane Separate Freeways at Toll Station Area: A Case Study from Huludao Toll Station on Shenshan Freeway," Sustainability, MDPI, vol. 11(11), pages 1-22, May.
    7. Guangyang Hou, 2023. "Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    8. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    9. Xiaoyong Ni & Hong Huang & Ruiqi Li & Anying Chen & Yi Liu & Han Xing & Kai Liu & Ming Wang, 2022. "Synergistic Influence of Rainstorm and Waterlogging on Drivers’ Driving Behavior—An Experimental Study Based on High-Fidelity Driving Simulator," Sustainability, MDPI, vol. 14(14), pages 1-20, July.

    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. Liping Yang & Xiaohua Zhao & Yang Bian & Mengmeng Zhang & Yajuan Guo, 2024. "Effects of the Amount of Information from Navigation Voice Guidance on Driving Performance," Sustainability, MDPI, vol. 16(14), pages 1-18, July.

    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:gam:jsusta:v:11:y:2019:i:3:p:830-:d:203739. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.