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Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances

Author

Listed:
  • Muhammad Ijaz

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China)

  • Lan Liu

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China)

  • Yahya Almarhabi

    (Center of Excellence in Trauma and Accidents, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Arshad Jamal

    (Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia)

  • Sheikh Muhammad Usman

    (Department of Civil Engineering, CECOS University of I.T. & Emerging Sciences, Peshawar 25000, Pakistan)

  • Muhammad Zahid

    (College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

Abstract

Not wearing a helmet, not properly strapping the helmet on, or wearing a substandard helmet increases the risk of fatalities and injuries in motorcycle crashes. This research examines the differences in motorcycle crash injury severity considering crashes involving the compliance with and defiance of helmet use by motorcycle riders and highlights the temporal variation in their impact. Three-year (2017–2019) motorcycle crash data were collected from RESCUE 1122, a provincial emergency response service for Rawalpindi, Pakistan. The available crash data include crash-specific information, vehicle, driver, spatial and temporal characteristics, roadway features, and traffic volume, which influence the motorcyclist’s injury severity. A random parameters logit model with heterogeneity in means and variances was evaluated to predict critical contributory factors in helmet-wearing and non-helmet-wearing motorcyclist crashes. Model estimates suggest significant variations in the impact of explanatory variables on motorcyclists’ injury severity in the case of compliance with and defiance of helmet use. For helmet-wearing motorcyclists, key factors significantly associated with increasingly severe injury and fatal injuries include young riders (below 20 years of age), female pillion riders, collisions with another motorcycle, large trucks, passenger car, drivers aged 50 years and above, and drivers being distracted while driving. In contrast, for non-helmet-wearing motorcyclists, the significant factors responsible for severe injuries and fatalities were distracted driving, the collision of two motorcycles, crashes at U-turns, weekday crashes, and drivers above 50 years of age. The impact of parameters that predict motorcyclist injury severity was found to vary dramatically over time, exhibiting statistically significant temporal instability. The results of this study can serve as potential motorcycle safety guidelines for all relevant stakeholders to improve the state of motorcycle safety in the country.

Suggested Citation

  • Muhammad Ijaz & Lan Liu & Yahya Almarhabi & Arshad Jamal & Sheikh Muhammad Usman & Muhammad Zahid, 2022. "Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 19(17), pages 1-24, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10526-:d:896031
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    References listed on IDEAS

    as
    1. Muhammad Zahid & Yangzhou Chen & Arshad Jamal & Khalaf A. Al-Ofi & Hassan M. Al-Ahmadi, 2020. "Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study," IJERPH, MDPI, vol. 17(14), pages 1-15, July.
    2. Magdalena Blanco & Jose Maria Cabrera & Felipe Carozzi & Alejandro Cid de Orta, 2022. "Mandatory Helmet Use and the Severity of Motorcycle Accidents: No Brainer?," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 187-218, June.
    3. Amjad Pervez & Jaeyoung Lee & Helai Huang & Xiaoqi Zhai, 2022. "What Factors Would Make Single-Vehicle Motorcycle Crashes Fatal? Empirical Evidence from Pakistan," IJERPH, MDPI, vol. 19(10), pages 1-14, May.
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    6. Tufail Ahmed & Mehdi Moeinaddini & Meshal Almoshaogeh & Arshad Jamal & Imran Nawaz & Fawaz Alharbi, 2021. "A New Pedestrian Crossing Level of Service (PCLOS) Method for Promoting Safe Pedestrian Crossing in Urban Areas," IJERPH, MDPI, vol. 18(16), pages 1-18, August.
    7. Abdulla I. M. Almadi & Rabia Emhamed Al Mamlook & Yahya Almarhabi & Irfan Ullah & Arshad Jamal & Nishantha Bandara, 2022. "A Fuzzy-Logic Approach Based on Driver Decision-Making Behavior Modeling and Simulation," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
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    Cited by:

    1. Thanapong Champahom & Chamroeun Se & Sajjakaj Jomnonkwao & Tassana Boonyoo & Vatanavongs Ratanavaraha, 2023. "A Comparison of Contributing Factors between Young and Old Riders of Motorcycle Crash Severity on Local Roads," Sustainability, MDPI, vol. 15(3), pages 1-24, February.
    2. Zhaoming Chen & Wenyuan Xu & Youyang Qu, 2023. "Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    3. Adnan Yousaf & Jianping Wu, 2023. "Motorcycle-Riding Experience: Friend or Foe? Understanding Its Effects on Driving Behavior and Accident Risk," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
    4. Arshad Jamal & Muhammad Ijaz & Meshal Almosageah & Hassan M. Al-Ahmadi & Muhammad Zahid & Irfan Ullah & Rabia Emhamed Al Mamlook, 2022. "Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions," Sustainability, MDPI, vol. 14(23), pages 1-13, November.

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