MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202641113511 A) filed by Dr. Srinivas Mulkalapalli; Dr. Jan Nisar Akhtar; Dr. M. Mohankumar; Dr. Charan K V; Dr G Purushothaman; N. Jayalakshmi; Dr. Syed Kaleem Afrough Zaidi; Dr. Prachee Sharma; S. Revathi; Dr. S. Sangeetha; Likhita Devineni; and Dayananda Sagar Academy Of Technology And Management on September 22, 2026, for Intelligent Smart City Route Management System Using Machine Learning And Graph-Based Shortest Path Prediction For Sustainable Economic Growth.
Inventors include Dr. Srinivas Mulkalapalli; Dr. Jan Nisar Akhtar; Dr. M. Mohankumar; Dr. Charan K V; Dr G Purushothaman; N. Jayalakshmi; Dr. Syed Kaleem Afrough Zaidi; Dr. Prachee Sharma; S. Revathi; Dr. S. Sangeetha; and Likhita Devineni.
The application for the patent was published on October 02, 2026, under issue no. 40/2026.
Abstract: Intelligent Smart City Route Management System Using Machine Learning and Graph-Based Shortest Path Prediction for Sustainable Economic Growth is the proposed invention. The proposed invention employs a Dynamic Spatio-Temporal Graph Transformer (DST-GT) to model an urban transportation network as a dynamically evolving graph comprising intersections as nodes and road segments as weighted edges. Real-time and historical data acquired from IoT sensors, connected vehicles, GPS devices, traffic cameras, weather sources, road infrastructure, and traffic-management systems are processed to generate Spatio-temporal traffic features. The DST-GT learns spatial dependencies and temporal variations in traffic flow and dynamically updates road-edge weights according to predicted congestion, travel time, incidents, road conditions, energy consumption, and emission-related parameters. A shortest-path optimization engine utilizes the predicted traffic states to generate and evaluate alternative routes according to travel efficiency and sustainability parameters. A feedback mechanism continuously compares predicted and observed traffic conditions and updates the graph representation for adaptive routing. The proposed system thereby facilitates predictive route management, reduced congestion, improved transportation efficiency, optimized energy utilization, reduced environmental impact, and enhanced sustainable urban mobility and economic productivity.
Disclaimer: Curated by HT Syndication.
