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Different factors have effects on traffic crashes and crash-related injuries.These factors include segment characteristics, crash-level characteristics,occupant level characteristics, environment characteristics, and vehicle levelcharacteristics. There are several studies regarding these factors' effects oncrash injuries. However, limited studies have examined the effects of pre-crashevents on injuries, especially for curve-related crashes. The majority ofprevious studies for curve-related crashes focused on the impact of geometricfeatures or street design factors. The current study tries to eliminate theaforementioned shortcomings by considering important pre-crash events relatedfactors as selected variables and the number of vehicles with or without injuryas the predicted variable. This research used CRSS data from the NationalHighway Traffic Safety Administration (NHTSA), which includes trafficcrash-related data for different states in the USA. The relationships areexplored using different machine learning algorithms like the random forest,C5.0, CHAID, Bayesian Network, Neural Network, C\&R Tree, Quest, etc. Therandom forest and SHAP values are used to identify the most effectivevariables. The C5.0 algorithm, which has the highest accuracy rate among theother algorithms, is used to develop the final model. Analysis results revealedthat the extent of the damage, critical pre-crash event, pre-impact location,the trafficway description, roadway surface condition, the month of the crash,the first harmful event, number of motor vehicles, attempted avoidancemaneuver, and roadway grade affect the number of vehicles with or withoutinjury in curve-related crashes.

Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes