MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085505 A) filed by Malla Reddy Engineering College For Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be University; and Malla Reddy Vishwavidyapeeth Deemed on July 13, 2026, for Enhanced Road Accident Prediction Accuracy With Real-Time Weather Intelligence.
Inventors include Dr. Y. Madhaveelatha; Dr. Gade Deepika; Mr. Sekhar Babu Golla; Dr. Deshoju Vemana Chary; Mr. S Sai Rajesh; Dr. B. Nageshwar Rao; Dr. Raghunadh Pasunuri; and Mrs. Sheetal Kulkarni.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Road accidents have become a major global concern due to the rapid increase in vehicles and urban traffic congestion. Every year, millions of people are injured or lose their lives due to traffic accidents. Traditional accident analysis methods rely mainly on historical data and static parameters, which often fail to capture real-time environmental influences. Existing accident prediction systems usually focus on historical accident datasets and apply statistical or machine learning techniques to identify patterns. However, these systems lack the capability to incorporate dynamic environmental factors such as weather conditions, fog, rainfall, and temperature variations, which significantly affect road safety. Weather conditions are known to influence driver visibility, road friction, vehicle control, and traffic flow. For instance: • Rain can cause slippery roads • Fog reduces drivSertr onvgis ibwiilitndys • can affect vehicle stability • Extreme temperatures can affect road surface conditions Despnite fluenthceese s, mi any current accident prediction models do not integrate real-time weather intelligence, leading to limited prediction accuracy. Therefore, there is a need for an intelligent accident prediction system that integrates machine learning models with real-time weather data to provide more reliable predictions and support proactive road safety measures. Experimental results show that integrating real-time weather intelligence improves prediction reliability and system adaptability compared to traditional static models. The invention can be deployed in smart transportation systems, traffic monitoring centers, emergency response planning, and intelligent city infrastructures
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