IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i5p2122-d1350898.html
   My bibliography  Save this article

Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data

Author

Listed:
  • Zhenzhen Yang

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    Beijing PalmGo Infotech Co., Ltd., Beijing 100085, China)

Abstract

Dangerous or illegal driving may disrupt the traffic safety management of public security organs, damage road infrastructure, lead to traffic accidents, or result in economic losses. This paper proposes a framework based on China’s highway toll data to identify dangerous or illegal driving risks, such as unfamiliarity with road conditions, overload, driving over the speed limit, fatigued driving, fake license plates, and other risks. The unfamiliarity with road conditions is identified with the frequency of driving routes. When the total weight of a vehicle and its cargo is greater than the upper limit of the total weight of the vehicle and its cargo, the vehicle can be judged as overloaded. When the actual travel time is less than the minimum travel time, it can be inferred that the vehicle has a risk of fatigued driving, driving over the speed limit, a fake license plate, or other risks. Two accidents are used to demonstrate the process of the proposed framework for identifying driving risks based on China’s highway toll data. Additional analysis proves that the proposed framework can be used to identify dangerous or illegal driving risks, and it provides a scientific basis for the traffic safety management of public security organs, reducing infrastructure damage, and avoiding the loss of national taxes and fees.

Suggested Citation

  • Zhenzhen Yang, 2024. "Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data," Sustainability, MDPI, vol. 16(5), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2122-:d:1350898
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/5/2122/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/5/2122/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rahman, Mashrur & Sciara, Gian-Claudia, 2022. "Travel attitudes, the built environment and travel behavior relationships: Causal insights from social psychology theories," Transport Policy, Elsevier, vol. 123(C), pages 44-54.
    2. Ahmed Alkhoori, Fatima & Kumar Maghelal, Praveen, 2021. "Regulating the overloading of heavy commercial Vehicles: Assessment of land transport operators in Abu Dhabi," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 287-299.
    3. Gang Zhong & Tingting Yin & Jian Zhang & Shanglu He & Bin Ran, 2019. "Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data," Transportation, Springer, vol. 46(5), pages 1713-1736, October.
    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. Yao, Haifang & Huang, Yingying & Liu, Jinsong, 2023. "Study on travel behavior characteristics of air passengers in an airport hinterland," Journal of Air Transport Management, Elsevier, vol. 112(C).
    2. Duan, Zhengyu & Zhao, Haoran & Li, Zhenming, 2023. "Non-linear effects of built environment and socio-demographics on activity space," Journal of Transport Geography, Elsevier, vol. 111(C).
    3. Wei Wu & Binxia Xue & Yan Song & Xujie Gong & Tao Ma, 2023. "Investigating the Impacts of Urban Built Environment on Travel Energy Consumption: A Case Study of Ningbo, China," Land, MDPI, vol. 12(1), pages 1-19, January.
    4. Oscar Lopez Jaramillo & Joel Rinebold & Michael Kuby & Scott Kelley & Darren Ruddell & Rhian Stotts & Aimee Krafft & Elizabeth Wentz, 2021. "Hydrogen Station Location Planning via Geodesign in Connecticut: Comparing Optimization Models and Structured Stakeholder Collaboration," Energies, MDPI, vol. 14(22), pages 1-26, November.
    5. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    6. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Zhi, Danyue & Song, Dongdong & Chen, Yan & de Bok, Michiel & Tavasszy, Lóránt A. & Gao, Ziyou, 2023. "Uncovering and modeling the hierarchical organization of urban heavy truck flows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    7. Qi Chen & Yibo Yan & Xu Zhang & Jian Chen, 2022. "Impact of Subjective and Objective Factors on Subway Travel Behavior: Spatial Differentiation," IJERPH, MDPI, vol. 19(23), pages 1-17, November.
    8. Qian, Chen & Li, Weifeng & Duan, Zhengyu & Yang, Dongyuan & Ran, Bin, 2021. "Using mobile phone data to determine spatial correlations between tourism facilities," Journal of Transport Geography, Elsevier, vol. 92(C).
    9. Huiying Wen & Yingxin Du & Zheng Chen & Sheng Zhao, 2022. "Analysis of Factors Contributing to the Injury Severity of Overloaded-Truck-Related Crashes on Mountainous Highways in China," IJERPH, MDPI, vol. 19(7), pages 1-17, April.
    10. Fulu Wei & Danping Dong & Pan Liu & Yongqing Guo & Zhenyu Wang & Qingyin Li, 2022. "Quarterly Instability Analysis of Injury Severities in Truck Crashes," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
    11. Li, Xianghua & Deng, Yue & Yuan, Xuesong & Wang, Zhen & Gao, Chao, 2022. "Data-driven behavioral analysis and applications: A case study in Changchun, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    12. Arpan Kumar Kar & Sunil Kumar & P. Vigneswara Ilavarasan, 2021. "Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(4), pages 267-288, December.

    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:16:y:2024:i:5:p:2122-:d:1350898. 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.