Injury Severity Analysis of Rural Passenger cars Crashes Involving Head-on collision
محل انتشار: پنجمین کنفرانس بین المللی محاسبات نرم
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 36
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شناسه ملی سند علمی:
CSCG05_125
تاریخ نمایه سازی: 9 اردیبهشت 1403
چکیده مقاله:
This study conducts a thorough analysis of factors affecting the severity of head-on collisions involving passenger vehicles on rural roads in Guilan province, Iran. Employing the non-parametric machine learning technique CART (Classification and Regression Trees), the research models and interprets outcomes based on a dataset of ۱۸۸۹ rural crashes spanning the period from ۲۰۱۴ to ۲۰۲۰, sourced from the traffic center of the Guilan rural police department. The results highlight critical elements such as driver familiarity with the route, accident timing, weather conditions, and road characteristics as influential factors shaping collision severity. The findings provide a nuanced understanding of the complexities in road safety, shedding light on specific circumstances contributing to property damage, injury, or fatality. Beyond academic discourse, it guides policymakers, road safety authorities, and planners. Identifying influential factors facilitates targeted interventions, enhancing road safety in similar contexts.
کلیدواژه ها:
نویسندگان
Mohammad Rahmaninezhad Asil
Department of Civil Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
Iraj Bargegol
Department of Civil Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran,