Statistical analysis of motorcyclists' safety behavior and crash risks in Kuwait

Road crashes represent a significant global safety crisis, with motorcyclists, cyclists, and pedestrians involved in over half of all traffic fatalities, motorcyclists being particularly vulnerable as both potential causes and victims of crashes. In Kuwait, the increasing use of motorcycles, particularly for delivery services, has raised crash risks, highlighting the need for updated safety measures. This study analyzes the driving behavior of motorcyclists and car drivers, focusing on road usage, traffic violations, and licensing while identifying key factors contributing to motorcycle crashes through surveys, interviews, and historical records. A busy area of the city was chosen as the research site due to its high concentration of restaurants and heavy traffic from delivery motorcyclists, customers, and other road users. Data was collected from interviews with 103 motorcyclists, an online survey of 300 car drivers, and publicly available open-source national data. Advanced statistical methods were used for analysis, including the Vector Autoregressive Model, Structural Equation Modeling, and Principal Component Analysis. The findings revealed that motorcycle-related road crashes are influenced by factors such as the motorcyclist's gender, spatial awareness, perceptual challenges, and familiarity with motorcycle positioning. Conversely, crash frequency is associated with the driver's experience, right-of-way violations by other road users, traffic violations by motorcyclists, and road conditions. To mitigate motorcycle-related road crashes, the study recommends the development of an Exclusive Motorcycle Lane with an optimal width of 1.855 m in the selected research site. This step has the potential to significantly improve road safety, reduce accidents, and save lives, contributing to safer urban mobility in Kuwait. Similar measures could also be implemented in countries with a high risk of motorcycle-related road crashes. • Motorcycles are widely spread nowadays for delivery services. • Probability of accident from car and motorcycle drivers' sides were identified. • A survey, interview & statistical data on motorcycles, violations & licenses were used. • Advanced analytical methods such as VAR, SEM, and PCA were utilized. • Probability of accident depends on driver attributes & knowledge of motorcycle position.