# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
###### Abstract
In order to examine the crash database, many analytical methods have been applied in the literature. Several studies have attempted to forecast the severity of crashes in recent years [8]. Many studies have attempted to analyze and identify the primary factors that influence the severity of road crashes [9]. The strategies most typically employed to undertake such analyses have been linear models, nonparametric models, and data mining approaches. These methods aid in determining the cause of a traffic crash. Statistical methods have been frequently employed to forecast the severity of traffic crashes. The logistic regression model, the ordered probit model, and the mixed logit model have all been proposed by different researchers and have been used to evaluate relevant data from traffic crashes and to examine the impact of various variables on the severity of traffic crashes [2; 10; 11; 12; 13; 14; 15; 16]. These prediction models aim to predict the severity of traffic crashes. However, according to some researchers [7; 17], the majority of regression models have established underlying correlations between dependent and independent variables and their own set of underlying assumptions (i.e., linear relations between the variables). If these assumptions are not met, the model may produce inaccurate estimates of the chance of serious injury. According to the reported literature, machine learning outperforms traditional statistical approaches in prediction, aided by the accessibility to a vast quantity of datasets [18]. Moreover, although crashes are random events that happen in space and time [19], they also exhibit spatial dependency and spatial autocorrelation, which should be considered during their evaluation [20]. Hotspot techniques should be used to manage road safety initiatives that envision minimizing traffic crashes with limited cost, as proven by [21]. Similarly, Australia's national hotspot program helped reduce fatal crashes by 30% [22]. In Belgium and Denmark, hotspot applications showed similar performance [23]. The present study is focused on three objectives. Firstly, developing an accurate model for predicting crash severity for the study area. Such a model will be helpful for medical and emergency services to provide rescue services in a timely manner and analyze crashes with unknown severity. Secondly, identifying parameters that affect and increase the severity of crashes, and thirdly, an approach that seeks to identify crash hotspots by emphasizing places where crashes are most likely to occur under specific conditions has also been determined in this study. For the prediction of road crash severity, tree-based ensemble models (random forest and gradient boosting) and a logistic regression model are examined in this work. In addition to taking into account machine learning approaches, this study also makes an effort to analyze the spatial consequences of crashes in an effort to point out and identify potential hotspots utilizing various variables. As a result, machine learning models can identify hotspots rather than just depending on the number and location of crashes by learning data patterns in variables. The current study is important because it shows the value of using statistical methods and spatial autocorrelation to identify crash-intensive-prone zones and successfully applies these methods using data from crashes on rural and urban roads in the Saudi Arabian province of Al-Ahsa, over a period from 2016 to 2018. This effort will be beneficial for traffic management authorities to devise effective policies for reducing traffic crashes and mitigating their impacts. The rest of this paper Is structured as follows. A summary of the most recent research in this field is given in Section 2. Data source and data processing are explained along with descriptive statistics in Section 3. Machine learning models taken into account, in this paper, are described in Section 4. Section 5 presents exploratory data analysis, while Section 6 discusses the study's findings.