en Railway Transportation Railway Transportation پژوهشي Research <div style="text-align: justify;"><span style="font-size:11pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span style="font-size:10.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif">This study investigates the factors influencing the severity of accidents at highway-rail grade crossings in the United States and explores strategies to mitigate the risks to road vehicle drivers. Two approaches are employed for modeling accident severity: statistical methods, such as multinomial logistic regression, and machine learning techniques, including Extreme Gradient Boosting (XGBoost) and random forest algorithms. The analysis is based on data from the Federal Railroad Administration&rsquo;s database, covering a twelve-year period (2010-2022). The results identify several key controllable factors that significantly impact accident severity, including vehicle speed, the position of road users, visibility obstructions, the number of cars in the train, and the speed of the train. Among the models tested, XGBoost demonstrated superior accuracy in predicting accident severity compared to multinomial logistic regression and random forest. Based on the findings, several recommendations are proposed to reduce accident risk at grade crossings, such as lowering train speeds, implementing advanced speed control systems, enhancing lighting at crossings, improving barrier inspections, and optimizing train scheduling. These measures aim to enhance safety and minimize collision severity at highway-rail crossings. </span></span></span></span></span></span></span></div> severity of the incidents, grade crossings between roads and railways, XGBoost, random forest, multinomial logistic regression 9 23 http://ijrare.iust.ac.ir/browse.php?a_code=A-10-327-1&slc_lang=en&sid=1 Rayehe sadat Mousavi Rayehe_mousavi@rail.iust.ac.ir 180031947532846002897 180031947532846002897 No School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran Behnam Bagherian rostami Behnam_bagherian@rail.iust.ac.ir 180031947532846002898 180031947532846002898 No School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran Zahrs sadst Sanagostar Sanagostarzahra@rail.iust.ac.ir 180031947532846002899 180031947532846002899 No School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran mohammad ali Zayandeh roodi M_zayandehroodi@cmps2.iust.ac.ir 180031947532846002900 180031947532846002900 No School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran Zahra Saghian Saghian_z@mail.iust.ac.ir 180031947532846002901 180031947532846002901 No School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran Morteza Bagheri Morteza.bagheri@iust.ac.ir 180031947532846002902 180031947532846002902 Yes School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran