• GITS HODCSE

The dynamic nature of urban traffic makes traditional static routing techniques increasingly ineffective in the era of smart transportation networks. Using machine learning (ML) and artificial intelligence (AI), this research introduces a unique model for dynamic route rationalization that optimizes routing choices in real-time. The suggested system predicts traffic patterns and dynamically recommends the most effective routes by combining real-time traffic data, road conditions, weather information, and incident reports. The model continuously learns from real-time and historical data to increase routing efficiency and forecast accuracy by leveraging supervised and reinforcement learning algorithms. The framework also takes into account things like construction projects, road closures, and limitations unique to a given vehicle. Comparing experimental results to conventional GPS-based systems, it is evident that there are notable reductions in trip time, fuel consumption, and traffic congestion. Urban mobility planners, logistics firms, and smart cities could all benefit from this strategy. The model provides scalable real-world applications in fleet management, emergency response routing, public transportation planning, and intelligent transport systems (ITS), in addition to its scholarly contributions. It is ideal for both urban and semi-urban settings due to its flexibility in differentiating to different regions and traffic patterns. The use of cloud-based platforms, connected cars, and IoT sensors improves its scalability and reactivity. Through clever route optimization, this concept ultimately seeks to enable safer, quicker, and more environmentally friendly transportation.

COGNITIVE ROUTING : AI-BASED DYNAMIC PATH OPTIMIZATION IN REAL-TIME TRAFFIC ENVIRONMENTS

"COGNITIVE ROUTING : AI-BASED DYNAMIC PATH OPTIMIZATION IN REAL-TIME TRAFFIC ENVIRONMENTS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.261-266, June-2025, Available :http://www.jetir.org/papers/JETIRGW06042.pdf

"COGNITIVE ROUTING : AI-BASED DYNAMIC PATH OPTIMIZATION IN REAL-TIME TRAFFIC ENVIRONMENTS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. pp261-266, June-2025, Available at : http://www.jetir.org/papers/JETIRGW06042.pdf