Traffic incident prediction and classification system using naïve bayes algorithm

Abstract

The research paper proposes a Traffic Incident Prediction and Classification System using Naïve Bayes Algorithm (TIPCS) to proactively predict and classify traffic incidents, which can lead to improved incident management and traffic flow. The system utilizes real-time traffic data, including location, date and time, and Traffic incident prediction is the task of using historical and real-time data to forecast the occurrence of traffic incidents, such as accidents, congestion, or road closures, in the future. The system aims to determine whether an incident is likely to occur or not and to classify it accordingly. The system is trained on historical incident data and is continuously updated with new data to improve its accuracy over time. The Naïve Bayes Algorithm is used for incident prediction and forecast, by utilizing this algorithm, TIPCS can accurately predict and classify incidents at 70.03% accuracy. The proposed study has the potential to significantly improve incident management and traffic flow, ultimately benefiting both transportation officials and road users.