# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 2 Literature Review  
Traditional methods for predicting collisions and classifying their seriousness use statistical modeling [24]. Poisson, binomial, Poisson-lognormal, negative binomial, gamma, and zero-inflated regressions, negative multinomial models, generalized estimation equations, random effects models, and random parameters models have all been applied in this situation [25]. The Bayesian hierarchical binomial logit, Bayesian-ordered probit, log-linear model, extended-ordered logit, multinomial logit, multivariate probit, ordered probit, and ordered logit are among the models that have also been utilized [26] for the estimation of crash severity. Despite the advances made with these techniques, statistical modeling has limitations because each model includes its predetermined correlations between the dependent and independent variables a and assumptions [27]. Statistical modeling, according to [28; 29], necessitates assumptions about data distribution. Such premises could be false and, as a result, violated. On the other hand, the drawbacks of this strategy have been extensively investigated, presenting an opportunity to employ fresh approaches. Machine learning methods, such as decision trees (D.T.s), nearest neighbor classification (K.N.N. ), support vector machines (SVMs), evolutionary algorithms (E.A.s), deep learning models, and artificial neural networks (ANNs), have been employed to analyze data for a variety of road safety issues and devised as data analytic methods due to their capacity to handle enormous volumes of multidimensional data and overcome the shortcomings of statistical methodologies. Furthermore, the flexibility of machine learning modeling techniques, generalization and learning capabilities, and great predictive capacity led to its acceptance as an accurate, generic, and practical mathematical model in road safety. ANN is a useful tool for solving problems in a variety of fields. It can be utilized for traffic incident detection [30], public transportation [27; 31], and road planning [32]. ANN is a complex, non-linear, parallel processor with a natural tendency to store and retrieve experimental knowledge [33]. The possible non-linear relationships between injury severity levels and crash causes were modeled by [PERSON] et al. [34] using a number of ANN models. Bayesian neural network (B.N.N.) and ANN models have been used to research road safety concerns for many years. Although the multilevel network architecture of ANN and B.N.N.N. models are similar, they differ in their capacity to predict outcome variables [35]. [PERSON] et al. [36] employed ANNs to predict and estimate crash severity in urban roadways, as well as to uncover major crash-related parameters. Variables, such as roadway width and head-on collisions, were found to have an impact on the outcomes. The effectiveness of B.N.N. models for forecasting road crashes was evaluated by [PERSON], [PERSON], and [PERSON] [37] using data gathered on rural areas in Texas. [PERSON] et al. [17] used a Bayesian network to identify the elements that influence injury severity, which was divided into two categories: slightly hurt and killed/severely injured. [PERSON] [38] created a Bayesian network for injury severity analysis. The findings revealed that Bayesian networks could be used to model road crashes. It also highlighted some benefits of using a Bayesian network over a regression model, such as the ability to include more variables and a larger data set. The statistical learning theory underpins the support vector machine (SVM) approach [39]. SVM models [40; 41], a novel class of models based on structural risk reduction and statistical learning theory [42], have lately been developed for road safety studies. Furthermore, the findings revealed that SVM models outperform B.P.N.N. models (or are at least equal) and do not overfit the data. Nearest neighbor classification (K.N.N.) is a basic yet groundbreaking machine learning algorithm. In a prediction problem, the K.N.N.