Identification and analysis of offenders causing hit and run accidents using classification algorithms

Hit-and-run crashes are significant concern for many countries. Due to lack of information of offending vehicles it is difficult to understand dynamics of these crashes to have a prevention plan. The paper aims to identify the impacting vehicle in hit-and-run crashes. We studied fatal road crashes of New Delhi for eleven years (2006-2016) and found that approximately 40% fatal crashes are hit-and-run with unknown impacting vehicles. We proposed a framework using eleven different machine learning-based classification algorithms - Logistic-Regression, KNN, SVM-Linear and RBF-Kernel, Naïve-Bayes, Random-Forest, DecisionTree, AdaBoost, Multilayer-Perceptron, CART and Linear-Discriminant-Analysis. We found SVM-linear-kernel gave best results. Results reveal that cars, buses, and heavy vehicles are involved vehicles in hit-and-run crashes. Buses were primary cause leading to 39% of hit-and-run during 2006-2009 thereafter cars increased drastically. Our framework is robust and scalable to any city. The outcomes provide inputs to traffic engineers for better policy prescription and road user safety.