MUMBAI, India, Jan. 23 -- Intellectual Property India has published a patent application (202541115021 A) filed by Sreeja Vijay Thelakkat; Pradeep Shanmugam; and S. A. Engineering College, Chennai, Tamil Nadu, on Nov. 21, 2025, for 'intelligent traffic control system using real-time sensor fusion, predictive analytics, and adaptive signal optimization.'
Inventor(s) include Sreeja Vijay Thelakkat; and Pradeep Shanmugam.
The application for the patent was published on Jan. 23, under issue no. 04/2026.
According to the abstract released by the Intellectual Property India: "In modern urban environments, the rapid increase in the number of vehicles has placed immense pressure on traditional traffic management systems. Conventional fixed-time traffic signals and manual monitoring methods are no longer sufficient to handle the dynamic and unpredictable nature of today's road networks. This has led to persistent challenges such as congestion, delays, fuel wastage, accidents, and rising pollution levels. To address these issues, cities around the world are shifting toward Intelligent Traffic Control, an advanced approach that integrates digital technologies with transportation infrastructure. As a crucial component of smart city initiatives, Intelligent Traffic Control aims to create safer, more sustainable, and more responsive transportation ecosystems. It represents a transformative leap from manual regulation to autonomous, data-driven traffic management, offering significant benefits for urban mobility, environmental health, and the quality of life for citizens. The invention discloses an intelligent traffic control system capable of autonomously optimizing traffic flow at intersections and across road networks using multi-source sensor fusion, real-time data processing, and predictive machine-learning models. The system integrates video, radar, LIDAR, vehicular GPS data, and environmental sensors to determine traffic density, queue length, and flow patterns, and adjusts signal timings accordingly using adaptive algorithms and reinforcement learning. The system further includes automated emergency vehicle prioritization, dynamic lane direction control, and city-level green-wave synchronization. This invention significantly improves traffic efficiency, reduces congestion and emissions, and enhances urban transportation safety."
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