DNN Classifier and Decision Tree-Based Novel Methodology for Analyzing Road Accidents

Nation's social and economic improvement is highly based on the citizen's health profile. Road safety can be considered as one of the most crucial and vital aspects of every country. The trauma of road accidents is, it mainly affects developing countries like Sri Lanka, rather than the developed countries. In Sri Lanka, 2.8% of the deaths are occurred by road accidents which indicate the number of 3554 deaths in last year and over 180 US $ million annually have to spend on road accidents. Furthermore, to reduce the danger of road accidents and to improve road safety, an analysis of road accidents based on machine learning was proposed. The primary aim of this study is to develop a mechanism to predict the driver fault at the accident and identify the accident hot spot in the area, which can be effectively used for taking countermeasures to improve road safety in Sri Lanka. Deep Neural Networks (DNN) classifier, Decision tree, Linear classifier in deep learning have been used to accurately predict and uncover road accident patterns within the data set. Results revealed that the Decision Tree and the DNN classifier are more accurate and comparable than other machine learning techniques used and the first 25 km of the road is more accident-prone rather the other road segments in the road. The research will provide the information needed to guide the relevant decision- makers in adopting suitable measures to prevent and to reduce the accident rate.