Predicting Accident-Prone Zones Using Machine Learning: A Web Application Approach

The increasing incidence of road accidents poses significant challenges to public safety and urban planning. Effective risk reduction techniques are necessary because traffic accidents represent a serious danger to public safety. In order to identify accident-prone areas, this research analyses historical traffic data, which includes information on accident instances, environmental factors, road characteristics, and traffic flow patterns. The goal of this project is to create an intuitive online tool that helps policymakers, traffic authorities, and urban planners visualize danger zones while collecting useful information. This paper presents a machine learning-based web application that predicts accident-prone zones and sends real-time notifications to users navigating these areas. With the application's dynamic user interface, users may see high-risk areas on a map and obtain comprehensive analytics to help them make wise decisions. By integrating historical accident data with geographic information, the application employs a Random Forest model to analyze and forecast risk zones, thereby enhancing road safety. User feedback demonstrates the application's efficacy and usability, revealing an accuracy of 97.63% in predictions and providing a proactive approach to accident awareness. Stakeholders may highlight initiatives by using the data, which identify important variables contributing to accidents. The objective of this tool is to improve road safety and lower the frequency of traffic accidents by enabling data-driven decision-making. Future work will be directed towards the integration of real-time traffic data and the enlargement of the model's domain to encompass diverse geographical areas.