Traffic Accident Risk Prediction

Road accidents remain a significant contributor to fatalities and injuries worldwide, resulting in considerable economic losses and public health issues. With the rise of urbanization and vehicle ownership, the intricacy of traffic systems and the likelihood of accidents also increase. Conventional approaches to traffic risk assessment typically depend on historical accident data and manual interventions, which can prove to be inefficient and reactive. To tackle this issue, this project presents a machine learning-based predictive system aimed at identifying and forecasting accident-prone areas or conditions in real time. The goal of this project is to develop a predictive model that can analyze traffic data and pinpoint high-risk scenarios that could result in accidents. The system utilizes various data sources, including historical accident records, real-time traffic flow, weather conditions, road types, time of day, and, if available, driver behavior data. Advanced machine learning techniques, such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting Machines, are employed on extensive datasets to estimate the probability of accidents occurring under various conditions. The proposed model comprises several essential components: data collection and preprocessing, feature selection, training and validation of predictive algorithms, and a user-friendly dashboard for visualizing predictions. The system is intended to issue alerts to traffic authorities and commuters when an elevated risk of accidents is identified in a specific area or time period. Additionally, it allows city planners and law enforcement agencies to implement preventive measures in a proactive manner.