A performance analysis of prediction techniques for impacting vehicles in hit-and-run road accidents

Road accidents are globally accepted challenges. They are one of the significant causes of deaths and injuries besides other direct and indirect losses. Countries and international organizations have designed technologies, systems, and policies to prevent accidents. However, hit-and-run accidents remain one of the most dangerous types of road accidents as the information about the vehicle responsible for the accident remain unknown. Therefore, any mechanism which can provide information about the impacting vehicle in hit-and-run accidents will be useful in planning and executing preventive measures to address this road menace. Since there exist several models to predict the impacting unknown vehicle, it becomes important to find which is the most accurate amongst those available. This research applies a process-based approach that identifies the most accurate model out of six supervised learning classification models viz. Logistic Reasoning, Linear Discriminant Analysis, Naive Bayes, Classification and Regression Trees, k-Nearest Neighbor and Support Vector Machine. These models are implemented using five-fold and ten-fold cross validation, on road accident data collected from five mid-sized Indian cities: Agra, Amritsar, Bhopal, Ludhiana, and Vizag (Vishakhapatnam). This study investigates the possible input factors that may have effect on the performance of applied models. Based on the results of the experiment conducted in this study, Support Vector Machine has been found to have the maximum potentiality to predict unknown impacting vehicle type in hit-and-run accidents for all the cities except Amritsar. The result indicates that, Classification and Regression Trees have maximum accuracy, for Amritsar. Naive Bayes performed very poorly for the five cities. These recommendations will help in predicting unknown impacting vehicles in hit-and-run accidents. The outcome is useful for transportation authorities and policymakers to implement effective road safety measures for the safety of road users.