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A Comparative Study on Machine Learning-based Approaches for Improving Traffic Accident Severity Prediction

Traffic accidents are the leading cause of many deaths, property damages, injuries, and fatalities as well as financial losses every year. Accurate traffic accident severity prediction would be very crucial to evaluate the major determinants associated with road accidents, offer precautions before occurrence based on the predicted outcomes and thus minimize all negative impacts caused by accidents. In the past decades, traditional techniques and machine learning have been used to predict traffic accidents. However, machine learning models are criticized because they perform like black box and lack interpretations for humans. The main purpose of this research is to employ machine learning-based approaches to predict crash injury severity and analyze the most influential factors contributing to road crashes as well as giving recommendations to concerned stakeholders. In this study, four classification approaches were employed: Random Forest (RF), Multinomial Naïve Bayes (MNB), K-Means Clustering (KC), and K-Nearest Neighbors (KNN) to predict accident severity and analyze feature importance. On the road accident dataset from 2015 to 2020 provided by the State of Victoria in Australia, the RF outperformed the remaining methods in terms of accuracy, precision, recall, and F1 Score. Month, time of day, female drivers, male drivers, total persons, speed zone, day of the week, passengers, etc., were found as the major determinants of accident severity. The accuracy enhanced model can help in giving recommendations such as safe route planning, preparing emergency vehicle allocation, reducing property damage, placing additional signage where necessary, and roadway design to concerned stakeholders to eradicate the number of fatalities and injuries resulting from traffic accidents.

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