Identification of factors and classifying the accident severity in Colombo - Katunayake expressway, Sri Lanka

Abstract

Sri Lanka’s expressway system was launched in 2011 and now owns three major expressways. Many peoples choose expressways rather than normal ways due to the reasons of time, traffic, easy of driving, etc. According to police reports of highway main traffic police branch, in recent years the number of accidents occurring in expressways is increasing drastically. Nowadays, the rate of accident occurrence in Colombo-Katunayake Expressway is high compared to the other two expressways and there was no previous research has been done in Sri Lanka regarding accidents on ColomboKatunayake expressway. Therefore, the objective of the study was to identify the factors contributing to accidents on the Colombo-Katunayake Expressway and to develop appropriate machine learning models to classify the severity of the accidents. In this study, 704 total accident cases were considered during the period 2013-2019. Chi-square test, logistic regression, and Kruskal–Wallis tests were used to identify the association between the accident severity and other influential variables found from the literature. Finally, seven variables: time category, driver’s age category, vehicle type, the reason for the accident, number of vehicles involved, cause for accident and rainfall were identified as influencing variables to accident severity under 5% level of significance. Naïve Bayes classification algorithm and probabilistic neural network (PNN) were used in the study to forecast accident severity. A random under-sampling technique was used to overcome the class imbalanced problem persists in the data set considered in the study. The final models developed using the Naïve Bayes algorithm and PNN exhibit 72.14% and 74.29% overall classification accuracy respectively. Both aforementioned models can be considered as suitable models to forecast accident severity in the Colombo-Katunayake expressway where the PNN model exhibits slightly higher accuracy. The final models developed by this study can be used to implement safety improvements against traffic accidents in expressways of Sri Lanka.