MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641088648 A) filed by Keshav Memorial Institute Of Technology on July 21, 2026, for Ai Based Netwrok Traffic Prediction: A Real-Time Intelligent Traffic Monitoring, Analysis, And Prediction System Using Map Apis And Data Visualization Techniques.
Inventors include Ms. C. Rohini; Ms. Nidhi Srivastav; Dr. G Narender; Ms. Avidi Dhanalakshmi; Ms. Konduru Manushri; Mr. Penkula Mahesh Kumar; and Ms. Uppelli Sunny.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: The present invention relates to an intelligent real-time traffic monitoring, analysis, and prediction system that integrates advanced mapping technologies, geospatial data processing, and analytical visualization into a unified web-based platform. The system leverages live traffic data obtained from mapping service providers such as TomTom APIs and processes it using a Python-based analytical framework to deliver accurate and real-time traffic intelligence. The invention enables users to search and monitor traffic conditions for any geographic location and visualize congestion through full road-level color-coded mapping, where traffic density is represented using intuitive color indicators such as red for heavy congestion, orange for moderate traffic, and green for smooth flow. This approach provides a clear and immediate understanding of traffic conditions compared to traditional marker-based systems. In addition to real-time visualization, the system includes analytical dashboards and graphical representations that display traffic density, speed variation, and congestion indices. These visual analytics allow users to interpret traffic patterns, identify peak congestion zones, and make informed travel decisions. The system also generates automated descriptive insights that explain traffic conditions in simple language, enhancing usability for both technical and non-technical users. The invention further incorporates predictive capabilities by analyzing current traffic trends and estimating future congestion patterns. This enables proactive travel planning, route optimization, and efficient logistics management. The platform is designed to be scalable, web-based, and globally accessible, making it suitable for smart city infrastructure, transportation authorities, logistics companies, and daily commuters. By improving route planning, reducing travel time, lowering fuel consumption, and minimizing environmental impact, the invention contributes significantly to sustainable urban mobility and intelligent transportation systems. Overall, the proposed system provides a comprehensive and user-friendly traffic intelligence solution that enhances decision-making, improves transportation efficiency, and supports the development of smart and connected cities.
Disclaimer: Curated by HT Syndication.
