Using data mining techniques to analyze crash patterns in Sri Lanka road accident data

Abstract

Abstract The road safety has been identified as a major factor that influences the sustainable development worldwide. This growing interest in road safety, is reflected by including it in Sustainable Development Goals of United Nations as “Halve the number of global deaths and injuries from road traffic accidents by 2020”. According to road accident statistics published by Sri Lanka traffic police in 2015, every three and half hours a person is killed due to a road accident and two are seriously injured. This shows that travelling on local roads becoming more and more unsafe and risky. When improving the road safety conditions, it is necessary to identify the major factors contributing to road crash injuries and deaths, in order to take appropriate safety measures. The Sri Lanka Police department uses MAAP (Microcomputer Accident Analysis Package) system for the storage and analysis of Road Traffic Accidents (RTA) data. However MAAP has its own limitations of analysis of accident data. In the area of road traffic accident analysis, data mining technique has been recognize as a reliable technique which can be used beyond the conventional techniques. When analyzing road traffic accidents, different models were developed to identify factors affecting the severity of a traffic accident. The objectives of this study are to explore the underlying factors influencing on injury severity, to identify the human, environment and vehicle factors influencing the road traffic accident severity and to identify crash proneness of road segments using available road and crash factors. In this study, data mining classification model is used to detect factors which influence on road accidents. We conducted an experiment with road accident data in 2015, provided by Sri Lanka Police. In this research we proposed an accident severity model based on selected data mining techniques to identify influential factors for the severity of road traffic accidents. The solution model is developed using Weka software tool.