# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 2 Literature Review
In another study, [PERSON] and [PERSON] (2010) [55] investigated crucial roadway-related characteristics that can influence the severity of road crashes. They created decision rules for road safety measures using D.T., NB, and K.N.N. They were primarily concerned with drivers and pedestrians, with no regard for other factors such as time or speed. They did not report the accuracy parameters of their machine learning models in detecting crash severity risks. A developing nonparametric tree-based model called the boosted regression tree model can capture the non-linear effects of both discrete and continuous variables without preprocessing the data. Using 5-year crash information for provincial highways in Ontario, Canada, [PERSON] and [PERSON] (2015) [56] employed the boosted regression tree model to examine the severity of driver injury in addition to other nonparametric models. The boosted regression tree model's findings revealed a substantial correlation between the severity of driver injuries and vehicle ejection and head-on crashes. They found that for both single-vehicle and two-vehicle crashes, the boosted regression tree model predicted driver injury severity more accurately than the classification and regression tree model. In order to determine the variables influencing the severity of pedestrian crashes in the Melbourne metropolitan region, where mid-block collisions accounted for 46% of all pedestrian collisions and mid-block collisions were the scene of 49% of pedestrian fatalities, [PERSON] et al. (2017) [57] developed three models employing various decision trees (D.T.s). They employed bagging and boosting approaches to enhance the D.T.s' accuracy, stability, and resilience. According to the study's findings, the boosting strategy increased each D.T. model's accuracy by 46%. Additionally, the results of raising D.T.s demonstrated that neighborhood social traits were just as significant in impacting the severity of pedestrian collisions as traffic and infrastructural factors. A gradient boosting decision tree (G.B.D.T.) model was presented by [PERSON] et al. (2019) [58] to examine the combined effects of crash-causing elements on four road crash indicators (i.e., injuries, deaths, number of crashes, and the financial loss). The economic, demographic, and road network conditions of Zhongshan, China, from 2000 to 2016, are studied using a total of 27 detailed influential elements. The findings demonstrate that the G.B.D.T. outperforms other conventional machine learning approaches in terms of prediction accuracy, handling multicollinearity across explanatory variables and, more crucially, ranking the influential factors on road crash prediction. The outcomes also demonstrate that there are parallels and variances among the most important determining factors for the crash indicator. Moreover, [PERSON] et al. (2021) [59] applied a scaled stacking gradient boosting decision tree (SS-GBDT) for predicting bus passenger flow, which can also be used for road crash prediction. To analyze non-intersection accident severity data, [PERSON] and [PERSON] (2009) [60] used association rule (or market basket analysis) learning directly for the first time. The analysis's findings provide straightforward criteria that show which crash features are related to one another. Using non-intersection crash data from the state of Florida for 2004, the application is illustrated. Later, [PERSON] et al. (2013a) [9] used association rules to draw out practical decision rules from the output of D.T. modeling. The percentage of samples that fit the rule among those that solely fit the left side, confidence, and lift was used to establish the importance of each generated rule. Support measures how often the rule occurs in the dataset (the statistical dependence of the rule).