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.

This research demonstrates the results of an investigation into the California bearing ratio (CBR) of granular soils from Qassim region, Saudi Arabia, using multilinear regression (MLR), pure quadratic (PQ) models, and gene expression programming (GEP) methods utilized to develop mathematical models for estimating the CBR based on basic soil index properties. In this study, samples were collected from different borrowing pits in the Qassim area. Forty-three samples of soil were taken and transferred to a laboratory for examination. Seven multilinear regressions and seven PQ models were investigated, while four GEP models were made. The selection of each model variable depends on soil indices, grouping into grain size distribution, Atterberg limits, and compaction parameters. The results of this analysis showed that the PQ model had a higher accuracy [coefficient of determination (R2) = 0.89, root mean square error (RMSE) = 16.006, uncertainty (U95) = 16.17, and reliability = 57%] than the multilinear regression model, which has a lower accuracy model [R2 = 0.811, RMSE = 20.791, U95 = 15.569, and reliability = 51%]. The best GEP model yields [R2 = 0.776, RMSE = 22.552, U95 = 15.787, and reliability = 53%]. Furthermore, sensitivity analysis was conducted to distinguish the influences of different input variables on CBR; it was found that fines percentage (F200), maximum dry density (MDD), and optimum moisture content (OMC) are the most influential variables.