# Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
###### Abstract  
Kingdom of Among the G20 countries, Saudi Arabia (KSA) is facing alarming traffic safety issues compared to other G-20 countries. Mitigating the burden of traffic accidents has been identified as a primary focus as part of vision 20230 goals. Driver distraction is the primary cause of increased severity traffic accidents in KSA. In this study, three different machine learning-based severity prediction models were developed and implemented for accident data from the Qassim Province, KSA. Traffic accident data for January 2017 to December 2019 assessment period were obtained from the Ministry of Transport and Logistics Services. Three classifiers, two of which are ensemble machine learning methods, namely random forest, XGBoost, and logistic regression, were used for crash injury severity classification. A resampling technique was used to deal with the problem of bias due to data imbalance issue. SHapley Additive exPlanations (SHAP) analysis interpreted and ranked the factors contributing to crash injury. Two forms of modeling were adopted: multi and binary classification. Among the three models, XGBoost achieved the highest classification accuracy (71%), precision (70%), recall (71%), F1-scores (70%), and area curve (AUC) (0.87) of receiver operating characteristic (ROC) curve when used for multi-category classifications. While adopting the target as a binary classification, XGBoost again outperformed the other classifiers with an accuracy of 94% and an AUC of 0.98. The SHAP results from both global and local interpretations illustrated that the accidents classified under property damage only were primarily categorized by their consequences and the number of vehicles involved. The type of road and lighting conditions were among the other influential factors affecting injury s severity outcome. The death class was classified with respect to temporal parameters, including month and day of the week, as well as road type. Assessing the factors associated with the severe injuries caused by road traffic accidents will assist policymakers in developing safety mitigation strategies in the Qassim Region and other regions of Saudi Arabia. title=1  
(80%) of all deaths in developing countries are from traffic accidents involving men. The Kingdom of Saudi Arabia (KSA) has faced serious traffic safety issues, particularly since the oil boom in the early 1970s. With over 300,000 road accidents occurring annually [3], the KSA surpasses all the G20 countries [3; 4]. The KSA suffers through a loss of SAR 13 billion annually associated with RTAs [4], as 30% of hospital capacity is affected. Therefore, it is critical to understand what precedes a road traffic accident so that a reduction in road trauma prevalence and severity can be achieved with effective interventions. The top five provinces in Saudi Arabia in terms of RTA frequency are Riyadh, Jeddah, Makkah, Madinah, and Qassim [5]. With the lowest population among these top five provinces, the Traffic Police Department in Qassim recorded more than 18,000 accidents in 2010. Around 23,000 people were involved in these accidents, leading to 2000 people being injured and the deaths of nearly 370 people [5]. There are few studies available in the current literature exploring crash severity in different cities in the KSA. Furthermore, Qassim Province is one of the 13 administrative provinces of the KSA, and has a high road traffic accident rate, similar to other provinces in the Kingdom [5]. Despite the growing traffic safety situation, no comprehensive study has been undertaken in recent times to assess the causes of these accidents and their severity in Qassim Province. To fill this gap, this research aims to develop a model for traffic accident severity in Qassim Province and identify the factors contributing to severity. To model crash injury severity, two ensemble machine learning (ML) methods, random forest and XGBoost, as well as logistic regression are proposed.