Abstract:

Road accidents represent a major public safety concern worldwide, causing significant loss of life, injury, and economic damage. The ability to predict road accidents with high accuracy can play a pivotal role in mitigating these risks and enhancing traffic safety. This research proposes a road accident prediction model using advanced data mining techniques. By analyzing historical accident data, weather conditions, traffic patterns, and other relevant variables, the model identifies key factors that contribute to accidents. Several data mining algorithms, including decision trees, support vector machines (SVM), and neural networks, are employed to build and evaluate predictive models. The study demonstrates how these techniques can be used to analyze large datasets and detect patterns that are indicative of potential accident hotspots and high-risk conditions. The results show that the model significantly improves accident prediction accuracy compared to traditional methods. Furthermore, the proposed system can be integrated into real-time traffic management systems to provide early warnings and facilitate proactive safety measures. This research highlights the potential of data mining in traffic safety management and paves the way for smarter, data-driven approaches to reducing road accidents.

Description:

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