Road accidents continue to be a major concern in India, causing injuries, loss of life, and economic setbacks. With increasing traffic density and unpredictable weather patterns, the risks on roads are only growing. While traditional measures focus on reacting to accidents after they occur, recent advancements in technology raise a new question—can we predict accidents before they happen? This is where machine learning, a branch of artificial intelligence, opens up exciting possibilities by analyzing data to identify patterns and forecast potential risks.This explores how traffic and weather data can be used together to build a predictive system that anticipates road accidents in advance. By collecting data such as vehicle count, speed, road surface condition, rainfall, humidity, visibility, and temperature, it becomes possible to understand the environment in which accidents are more likely to occur. Machine learning algorithms are then trained on this combined dataset to recognize warning signs from previous accident patterns and use them to forecast future incidents.
Several well-known machine learning models have been tested for this task, including Decision Trees, Random Forest, K-Nearest Neighbor, Naive Bayes, and Logistic Regression. Among these, Random Forest stood out due to its ability to manage complex and diverse data with high accuracy. The results show that when traffic data is combined with weather-related factors, the prediction becomes more precise, helping identify high-risk conditions with greater confidence.The purpose of such a system is not just academic. If implemented in real-time systems, it can help traffic management teams, emergency services, and even navigation apps to take early action. Roads with a high chance of accidents can be flagged, drivers can be alerted, and rescue services can be better prepared. Over time, this could lead to smarter cities with fewer accidents and safer travel for everyone.
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