# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 2 Literature Review
classifies an observation based on the k-nearest observations [43]. For classification problems, the D.T. technique is highly beneficial. A training set of inputs and outputs (i.e., classes) is produced during the development of a tree. D.T.s are data mining techniques [44; 45], which can be utilized to address classification difficulties. One of the key advantages of D.T.s is that their structure allows for the extraction of 'if-then' decision rules (D.R.s). Such DRs may reveal behaviors that are unique to a particular data collection. They are not only practical but also simple to comprehend from the standpoint of safety analysis [46]. Evolutionary algorithms (E.A.s) are based on natural selection processes, in which only the strongest individuals survive [47], and use stochastic search strategies. The two fundamental types of evolutionary algorithms are genetic algorithms and genetic programming, and they are particularly useful for optimizing problems typically related to other approaches. Using vast data gathered on unsignalized crossings in Florida, multivariate adaptive regression splines (MARSs), a recently created machine learning technique, was used by [PERSON] and [PERSON] [48] to predict vehicle angle collisions. The data showed that MARS performed better than the N.B. models. After using R.F. to screen the covariates, the suggested MARS models produced promising results. According to the data, MARS appears to be an effective method for forecasting collisions at unsignalized intersections. Deep learning is a novel approach that emerged from the advancement of machine learning. It examines many layers of non-linear information, both supervised and unsupervised, to interpret or categorize patterns [49]. A kind of machine learning called \"deep learning\" creates models that can extract traits from the most fundamental to the most apparent layers [50]. This capability eliminates the difficulty of raw data processing, which is a typical issue with many machine learning algorithms. Deep neural networks (D.N.N.s), convolutional neural networks (CNNs), and recurrent neural networks (R.N.N.s) are the most common examples of this approach, which are used in speech recognition, visual object recognition, and object detection [44]. In contrast to earlier machine learning techniques, which were constrained in their ability to read natural data in its raw form, deep learning develops computer models to extract inherent qualities from data at various levels. Deep learning applications in the field of transportation are limited, with only one study focusing on traffic flow prediction [51], despite the fact that deep learning methods have proven exceptional performance in a number of applications [52]. [PERSON] et al. [36] employed ANNs to predict and estimate crash severity in urban roadways, as well as to uncover major crash-related parameters. The results showed that the most important factors that increase crash severity on urban highways include variables such as highway width, head-on collisions, the type of vehicle at fault, disregarding lateral clearance, keeping track of distance, inability to control the vehicle, exceeding the permitted speed, and driver deviation to the left. [PERSON] et al. [51] used SVM and multi-layer perception for predicting crash severity. They only used a small number of data samples to test their hypothesis. They looked at only two variables: speed and alcohol as major factors in car crashes. The SVM outperformed with a 94% accuracy rate. They stated that high-speed driving while inebriated was the cause of the crash. [PERSON] et al. [53] used decision tree (D.T. ), N.B., and SVM, as well as S.O.M. and k-modes, for clustering. They obtained superior results with the clustered dataset. For traffic agencies, [PERSON] et al. [54] employed N.B., AdaBoost, R.F., and logistic regression (L.R.) to locate highways with high crash risk. They used A.U.C., R.O.C., recall, precision, and F-measure to evaluate their models. The R.F. outperformed with a 75% accuracy rate.