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 1889 rural crashes spanning the period from 2014 to 2020, 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
کلیدواژهها
نویسندگان
Mohammad Rahmaninezhad Asil
Department of Civil Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
ایرج برگ گل
Department of Civil Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran,
شیوه ارجاع
Rahmaninezhad Asil, Mohammad and Bargegol, Iraj,1402,Injury Severity Analysis of Rural Passenger cars Crashes Involving Head-on collision,5th International Conference on Software Computing,Rudsar
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی محاسبات نرم16 اسفند 1402 · رودسر