MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641046881 A) filed by Buvana M; Kishore L; Abdul Gouse A; and Akilesh Prabhu S on April 13, 2026, for Traffic Predictor-A Big Data Driven Predictive Analytics Framework For Real Time Urban Transportation Optimization.
Inventors include Buvana M; Kishore L; Abdul Gouse A; and Akilesh Prabhu S.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: ABSTRACT Traffic-Predictor provides a scalable framework for real-time analysis and prediction of urban traffic conditions using big data processing and machine learning techniques. The system ingests transportation data continuously from multiple heterogeneous sources including traffic sensors, GPS-enabled vehicles, public transport systems, and weather CM E o services. The ingested data is subjected to distributed processing using big data frameworks to handle high volume, velocity, and variability. Predictive models are applied to identify congestion patterns, estimate travel times, detect accident-prone zones, and forecast traffic demand. The system further integrates interactive visualization mechanisms to present real-time traffic insights and optimal route recommendations. Decision - making is supported through datadriven predictive outputs, enabling proactive traffic management and improved commuter experience. The framework emphasizes scalability, real-time processing, and adaptability, making it suitable for deployment in modem smart city environments. M. I b i Si (I s Trust levels for entities are adjusted on an ongoing basis through integration of rule outcomes and graph-derived insights. When trust thresholds are violated or anomalous patterns are identified, the framework generates alerts for administrative attention. Emphasis is placed on explainable decision-making via transparent rules and traceable graph traversals, enabling security operators to understand the basis for each trust decision or alert without reliance on opaque computational methods. This approach supports proactive monitoring and response to threats including insider activities, lateral movement and multi-stage attacks in dynamic, ' distributed environment
Disclaimer: Curated by HT Syndication.
