IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i11p3864-d177992.html
   My bibliography  Save this article

A Cask Evaluation Model to Assess Safety in Chinese Rural Roads

Author

Listed:
  • Longyu Shi

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China)

  • Nigar Huseynova

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Bin Yang

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Chunming Li

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China)

  • Lijie Gao

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China)

Abstract

Suburban roads are an important part of China’s road network and essential infrastructure for rural development. Poorly designed road curves and scarcity of traffic signs have caused an excessively high traffic accident rate in plain topographical areas. In this study, an approach to evaluate and improve rural road traffic safety is introduced. Based on fuzzy and cask theory and weighted analysis, a cask evaluation model is built. It provides a quantitative instant method for analyzing road safety in the absence of traffic accident information or rigorous road space data, by identifying dangerous sections and key impact factors, and ultimately help to put forward traffic safety improvements. Based on the application to a specific section of Xiaodang Central Road in the Fengxian District of Shanghai, the result shows that the pavement conditions of cement-hardened dual-lane rural roads was good, but traffic safety was poor. Missing traffic signs, unreasonable road alignment, and poor roadside conditions were the main problems. Finally, improvements of the short-stave subsystem were proposed: the location of guide signs and roadside conditions should be improved, and the number and efficacy of the rural road traffic signs need to be increased, and markings should be and receive regular maintenance.

Suggested Citation

  • Longyu Shi & Nigar Huseynova & Bin Yang & Chunming Li & Lijie Gao, 2018. "A Cask Evaluation Model to Assess Safety in Chinese Rural Roads," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3864-:d:177992
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/11/3864/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/11/3864/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Lian Zhu & Linjun Lu & Wenying Zhang & Yurou Zhao & Meining Song, 2019. "Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    2. Spasoje Mićić & Radoje Vujadinović & Goran Amidžić & Milanko Damjanović & Boško Matović, 2022. "Accident Frequency Prediction Model for Flat Rural Roads in Serbia," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    3. Zeyang Cheng & Zhenshan Zu & Jian Lu, 2018. "Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections," Sustainability, MDPI, vol. 11(1), pages 1-17, December.
    4. Tianpei Tang & Senlai Zhu & Yuntao Guo & Xizhao Zhou & Yang Cao, 2019. "Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method," IJERPH, MDPI, vol. 16(7), pages 1-17, April.
    5. Dongkwan Lee & Jean-Michel Guldmann & Choongik Choi, 2019. "Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    6. Yichi Zhang & Xuan Dou & Hanping Zhao & Ying Xue & Jinfan Liang, 2023. "Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    7. Nopadon Kronprasert & Katesirint Boontan & Patipat Kanha, 2021. "Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    8. Antoni Wontorczyk & Stanislaw Gaca, 2021. "Study on the Relationship between Drivers’ Personal Characters and Non-Standard Traffic Signs Comprehensibility," IJERPH, MDPI, vol. 18(5), pages 1-19, March.

    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:10:y:2018:i:11:p:3864-:d:177992. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.