Comparative Analysis of Supervised Learning Algorithms for the Traffic Accident Prediction Under Rural and Urban Areas


  • Vinay Bhatt Assistant Professor, Department of Computer Applications, Maharaja Agrasen Himalayan Garhwal University (MAHGU), Pauri Garhwal, Uttarakhand, India


Machine learning, decision tree, SVM, random forest, regression model, accident predictions


The features of different mechanisms for safety to traffic accidents are the biggest challenge for the automobile manufacturing industry in rural and urban areas. The objective of this  work is to address the challenge of safety in traffic accidents by developing an accurate prediction model for identifying patterns in the different scenarios for preventing traffic accidents using accurate prediction. The machine learning algorithm is used to easily predict the traffic accident scenario and automatically identify data and patterns. By using an ML-based model, a cost-effective approach for safety measures was built. The aim of this prediction model is to improve the accurate prediction for preventing traffic accidents in security measures. In this prediction model, three ML-based algorithms namely random forest, decision tree and SVM were used to predict the data of traffic accidents with low-budget scientific measures for reduction of maximum possible accidents. The focus of this study is to achieve accurate data on traffic congestion. The random forest algorithm is a category of supervised learning algorithms in which part of the machine learning algorithm is best for the prediction of traffic accidents due to a higher accuracy rate when compared to other proposed algorithms such as SVM and decision tree, as concluded in this research work. In this work, large amounts of data related to traffic accidents on behalf of the time accident location, road features and weather conditions were collected. The target of this study is reducing traffic congestion and preventing traffic accidents.


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