# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
###### Abstract  
The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord \(G_{i}^{s}\) statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area's spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis's approaches, which included Getis Ord \(G_{i}^{s}\), the crash severity index, and the spatial autocorrelation of accident incidents according to Moran's I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety. crash severity; crash types; ensembles; machine learning; hotspots +
Footnote †: journal: Applied  
about ten times more than the number of deceased people, at around 240,000 cases per year. These high rates, combined with the significant share of passenger traffic, necessitate a thorough investigation into passenger safety [5]. For an intelligent transportation system to be deployed successfully and provide appropriate levels of medical care and transportation in a timely way, in the case of a traffic incident, an accurate and quick severity prediction algorithm is crucial. Different agencies may profit from the ability to estimate the severity of a reported incident with unknown severity or the severity of future crashes. Crash severity prediction is one of the most significant aspects of crash management; it helps rescuers assess the severity of traffic crashes and their potential impact and perform effective crash management methods. Injury severity is frequently regarded as a dependent (class/response) variable when studying traffic crash injury severity using historical crash records, whereas other crash components are referred to as independent (predictor) variables. Researchers use crash data to identify risk variables and execute effective interventions to improve road safety. Driver characteristics, roadway characteristics, vehicle characteristics, crash characteristics, and atmospheric elements are all factors that influence the severity and frequency of crashes [6; 7]. Crash data are compiled into a massive database that includes various crash-related characteristics.