Educational disparities shifts in motorcyclist injury severity: Accounting for unobserved heterogeneity
Traffic crashes involving motorcyclists are the dominant public health concern in developing countries. While existing literature has mainly focused on the severity of various risk factors contributing to motorcyclist injury severity, such as helmet use and road and environmental conditions, the rol...
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| Published in | Journal of transportation safety & security Vol. 17; no. 7; pp. 715 - 756 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
03.07.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1943-9962 1943-9970 |
| DOI | 10.1080/19439962.2024.2449118 |
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| Abstract | Traffic crashes involving motorcyclists are the dominant public health concern in developing countries. While existing literature has mainly focused on the severity of various risk factors contributing to motorcyclist injury severity, such as helmet use and road and environmental conditions, the role of the driver's educational background remains a relatively under-researched subject. Educational disparities can significantly impact the risk perception decision-making behavior of motorcyclists, and their complex interplay with other factors can potentially influence injury severity outcomes. Aiming to fill the highlighted research gaps, the current study employs a random parameter logit (RPL) approach by capturing the heterogeneity in individual responses to provide nuanced insights and relationships between motorcyclist educational disparities and their severity outcomes. Four RPL models that account for heterogeneity in means and variances of motorcyclist riders with different levels of education (illiterate, high school, college, and graduation) are developed. The empirical analysis is based on a database of three years (2020 to 2022) of motorcycle accidents maintained by RESCUE 1122 in Rawalpindi, Pakistan. Model assessment for heterogeneity in the means and variances highlighted unique indicators for riders with different educational backgrounds. Model results revealed that severe and fatal injury outcomes are affected by attributes including temporal indicators (weekend, day, year, winter, and autumn), rider profiles (male, young-, middle-, and older-ages), road characteristics (60-70 km/h, lane 3), weather conditions (raining, sunny), and vehicle and crash characteristics (crash with a truck or car, over-speeding, distractions, and slipping). Models' estimation results indicate notable differences among the injury-severity contributing factors for various RPL models, highlighting the significance of motorcyclists' educational backgrounds and unobserved heterogeneity. The recent findings can provide valuable insights and guide researchers and practitioners to develop effective countermeasures and targeted interventions to tackle the safety concerns associated with various motorcyclists' varying education levels. |
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| AbstractList | Traffic crashes involving motorcyclists are the dominant public health concern in developing countries. While existing literature has mainly focused on the severity of various risk factors contributing to motorcyclist injury severity, such as helmet use and road and environmental conditions, the role of the driver's educational background remains a relatively under-researched subject. Educational disparities can significantly impact the risk perception decision-making behavior of motorcyclists, and their complex interplay with other factors can potentially influence injury severity outcomes. Aiming to fill the highlighted research gaps, the current study employs a random parameter logit (RPL) approach by capturing the heterogeneity in individual responses to provide nuanced insights and relationships between motorcyclist educational disparities and their severity outcomes. Four RPL models that account for heterogeneity in means and variances of motorcyclist riders with different levels of education (illiterate, high school, college, and graduation) are developed. The empirical analysis is based on a database of three years (2020 to 2022) of motorcycle accidents maintained by RESCUE 1122 in Rawalpindi, Pakistan. Model assessment for heterogeneity in the means and variances highlighted unique indicators for riders with different educational backgrounds. Model results revealed that severe and fatal injury outcomes are affected by attributes including temporal indicators (weekend, day, year, winter, and autumn), rider profiles (male, young-, middle-, and older-ages), road characteristics (60-70 km/h, lane 3), weather conditions (raining, sunny), and vehicle and crash characteristics (crash with a truck or car, over-speeding, distractions, and slipping). Models' estimation results indicate notable differences among the injury-severity contributing factors for various RPL models, highlighting the significance of motorcyclists' educational backgrounds and unobserved heterogeneity. The recent findings can provide valuable insights and guide researchers and practitioners to develop effective countermeasures and targeted interventions to tackle the safety concerns associated with various motorcyclists' varying education levels. |
| Author | Rehman, Zia Ur Jamal, Arshad Chaozhe, Jiang Wang, Chenzhu |
| Author_xml | – sequence: 1 givenname: Zia Ur surname: Rehman fullname: Rehman, Zia Ur organization: School of Transportation and Logistics, Southwest Jiaotong University – sequence: 2 givenname: Jiang surname: Chaozhe fullname: Chaozhe, Jiang organization: School of Transportation and Logistics, Southwest Jiaotong University – sequence: 3 givenname: Arshad surname: Jamal fullname: Jamal, Arshad organization: Department of Civil Engineering, College of Engineering, Qassim University – sequence: 4 givenname: Chenzhu surname: Wang fullname: Wang, Chenzhu organization: School of Transportation, Southeast University |
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