# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 7 Conclusions and Future Study  
This study aimed to develop and compare models for predicting the severity of road crashes in Al-Ahsa city of Saudi Arabia, based on the crash data from 2016 to 2018. Two machine learning techniques, namely, R.F. and XGBoost, and the logistic regression model, as a statistical technique, were used. Among the developed models, R.F. had the best performance in terms of accuracy, precision, recall, and F1 score. The overall model accuracy in the severity predictions of RF, XGBoost, and logistic regression had accuracies of 94%, 93%, and 79%, respectively. The feature importance score from R.F. model suggests that faulty tires, not giving way, sudden turning, and running over were among the most important causes of severe crashes. Over the past 20 years, methodologies for identifying crash hotspots have advanced, and they now play a critical role in implementing successful traffic safety management programs. In this study, the tendency of a crash pattern to cluster in space was determined using global autocorrelation analysis with Moran's I. The results demonstrate that high spatial dependence across the research region and the spatial patterns were clustered with a distance threshold of 500 m. The precise aggregated locations of traffic crashes across the research area were then determined using the local spatial autocorrelation Getis-Ord \(G_{i}^{*}\) statistic. The Getis-Ord \(G_{i}^{*}\) statistic successfully identified high-value clusters with a specified distance threshold. A region was not regarded as a hotspot if a high-value point was not surrounded by other high-value points. As a result, segments with high-value features could be predicted and given priority. The study will advise decision makers on the best places to invest or put safety measures in place. Figure 9: Composite spatial geographic distribution map of traffic crash hotspots based on the \(G_{i}^{*}\) score at the year level from 2016 to 2018. This study had some limitations with implications for future research and can be extended in the future from various perspectives. First, for the crash severity, there were only two available classes (fatal and serious injury); P.D.O. was absent due to data scarcity, which otherwise consists of the biggest share of the crash severity class. Moreover, the analysis period used is from 2016 to 2018, which can be considered a short period for this study. This is because of the unavailability of an extended data set in the study area. Future studies can utilize detailed and comprehensive datasets containing information on other explanatory variables, such as road geometric features, weather conditions, driving behaviour, road conditions, etc. One of the ML models' criticisms is their inability to interpret the model. Machine learning interpretation techniques such as SHapley Additive exPlanations (S.H.A.P.) may be utilized to better visualize the feature sensitivity on the model output for a better model interpretation. Moreover, it is recommended for future studies to apply a larger dataset, including different cities of Saudi Arabia, along with more severe classes. In addition, we also recommend performing further analysis to investigate the temporal trends of crashes. In this regard, the use of time series analysis is recommended. Provided more data are available, deep learning techniques are also promising in crash severity studies. Conceptualization, M.K.I., I.R., R.A., U.G., M.M.R. and M.A. ; methodology and software, I.R. and M.K.I. ; validation, and formal analysis, I.R., U.G. and M.K.I. ; resources and data curation, M.K.I. ; writing--original draft preparation, review and editing, M.K.I., U.G., M.A., I.R., M.M.R. and R.A.; project administration, M.K.I. ; funding acquisition, M.K.I. All authors have read and agreed to the published version of the manuscript. This work was financially supported by Deanship of Scientific Research in the King Faisal University, Saudi Arabia [Grant 1668]. Not applicable.