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Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel

Author

Listed:
  • Shengdi Chen

    (School of Transport & Communications, Shanghai Maritime University, Shanghai 201306, China)

  • Shiwen Zhang

    (Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Yingying Xing

    (Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Jian Lu

    (Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

Abstract

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.

Suggested Citation

  • Shengdi Chen & Shiwen Zhang & Yingying Xing & Jian Lu, 2020. "Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel," IJERPH, MDPI, vol. 17(9), pages 1-15, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3155-:d:352814
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    Citations

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    Cited by:

    1. Al-Baraa Abdulrahman Al-Mekhlafi & Ahmad Shahrul Nizam Isha & Nicholas Chileshe & Mohammed Abdulrab & Anwar Ameen Hezam Saeed & Ahmed Farouk Kineber, 2021. "Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
    2. Lan Wu & Qi Shen & Gen Li, 2022. "Identifying Risk Factors for Autos and Trucks on Highway-Railroad Grade Crossings Based on Mixed Logit Model," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
    3. 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.
    4. Khaled Assi & Syed Masiur Rahman & Umer Mansoor & Nedal Ratrout, 2020. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    5. Arshad Jamal & Waleed Umer, 2020. "Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network," IJERPH, MDPI, vol. 17(20), pages 1-22, October.
    6. Weiwei Qi & Shufang Zhu & Jinsong Hu, 2022. "Correlation Analysis of Real-Time Warning Factors for Construction Heavy Trucks Based on Electrified Supervision System," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    7. Younshik Chung & Jong-Jin Kim, 2023. "Exploring Factors Affecting Crash Injury Severity with Consideration of Secondary Collisions in Freeway Tunnels," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    8. Chenming Jiang & Junliang He & Shengxue Zhu & Wenbo Zhang & Gen Li & Weikun Xu, 2023. "Injury-Based Surrogate Resilience Measure: Assessing the Post-Crash Traffic Resilience of the Urban Roadway Tunnels," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    9. Mahyar Madarshahian & Aditya Balaram & Fahim Ahmed & Nathan Huynh & Chowdhury K. A. Siddiqui & Mark Ferguson, 2023. "Analysis of Injury Severity of Work Zone Truck-Involved Crashes in South Carolina for Interstates and Non-Interstates," Sustainability, MDPI, vol. 15(9), pages 1-18, April.

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