A comparative study on traffic collisions severity using machine learning approaches

Abstract

Road Traffic collisions and congestion are amongst one of the most crucial issues in the modern world. Every year, traffic collisions cause multiple deaths and injuries. It leads to economic losses as well. According to WHO, approximately 1.35 million people are losing their lives, with 20 to 50 million people face non-severe injuries every year because of road collisions. Hence, there is a need to create a prediction system that can help determine relations between various factors such as climate, types of automobile, driving pattern etc., to predict the severity of the collisions. It helps to improve public transportation, allowing safer routes and thus avoid the chances of high severity cases to make the roads safer. Smart cities concept can be helpful to handle modern problems. Accurate Models for predicting collision severity has become a significant challenge for transportation systems. This research establishes a procedure for identifying important parameters affecting collision severity and creates a relationship between human and environmental factors using several Machine Learning (ML) techniques. Among different types of ML techniques, classification algorithms have been applied for categorizing the level of severity. Supervised algorithms such as Random Forest (RF), Decision Trees (DT), Logistic Regression and Naïve Bayes have been used. A comparative study among performance and accuracies of various algorithms is also mentioned. These algorithms were tested on a dataset that contains historic data for collisions in the U.S and their severity levels. This study's findings show Random Forest with the best accuracy and identify the time of day, duration of an collision, and Point of Interest (POI) features as the influential parameters.