Understanding motorcycle crash severity in Pakistan: Insights from machine learning and interaction effects

作者：Muhammad Junaid, Salaheddine Bendak, Shihan Luo, Chaozhe Jiang, Amjad Pervez · 发表于：KSCE Journal of Civil Engineering · 年份：2026 · DOI：10.1016/j.kscej.2026.100569 · 被引用次数：1 · 研究领域：Traffic and Road Safety、Injury Epidemiology and Prevention、Urban Transport and Accessibility

• Motorcyclists are at a greater risk of sustaining severe injuries or fatalities in road traffic crashes • The study utilized data from a nationally representative department, Rescue 1122, in Pakistan, covering the period from July 2020 and June 2023. • New Jersey barriers, crashes during off-peak hours, rider distraction, cloudy weather, and younger rider age are strongly associated with increased injury severity in these crashes. Motorcycle crashes are a leading cause of road traffic fatalities worldwide, particularly in low- and middle-income countries like Pakistan, where they represent a significant share of road deaths. However, existing research in Pakistan has often relied on self-reported or hospital-based data, typically characterized by small sample sizes, single-year analyses, and a limited focus on roadway geometric features. These limitations have resulted in an incomplete understanding of the factors influencing crash severity. This study aims to address these gaps by analyzing a comprehensive dataset of 15,557 motorcycle crash records from Rawalpindi, Pakistan, spanning the period from July 2020 to June 2023. The dataset includes detailed information on rider demographics, crash causes, vehicle types, weather conditions, and roadway geometry. To predict injury severity, various machine learning models were employed, including Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, Categorical ...