International Journal of Science, Engineering and TechnologyAuthors: Abhishek, Arsh Ahmed, Piyush Tiwari, Mr. Karmbir
Abstract: Urban traffic congestion significantly impacts emergency response systems, often causing delays that may result in loss of life and property. Traditional traffic signal systems rely on fixed timing or localized adaptive control, which lack coordination and predictive intelligence. This paper proposes RescueRoute, an AI-driven real-time adaptive traffic management system designed to prioritize emergency vehicles through intelligent multi-intersection coordination. The system integrates computer vision for vehicle detection, deep reinforcement learning for adaptive signal control, and vehicle-to-infrastructure communication for real-time data exchange. Unlike existing approaches, the proposed system introduces end-to-end route optimization, predictive traffic analysis, and dynamic green corridor generation. A hybrid architecture combining centralized intelligence with decentralized control ensures scalability and efficiency. Simulation using SUMO demonstrates significant performance improvements compared to traditional traffic systems. Experimental results show approximately 40–45% reduction in emergency response time, 35–45% reduction in average vehicle waiting time, and nearly 50% improvement in traffic throughput across multiple scenarios. These results validate the effectiveness of RescueRoute in improving emergency response efficiency while maintaining overall traffic flow. Furthermore, the proposed RescueRoute framework emphasizes key characteristics such as reliability, scalability, and adaptability, which are essential for modern intelligent transportation systems. The system is designed to function efficiently in highly dynamic urban environments. where traffic patterns change rapidly. By leveraging real-time data processing and AI-driven decision-making, the framework ensures consistent performance even under unpredictable conditions. These features make RescueRoute highly suitable for deployment in future smart city infrastructures, where automated and intelligent traffic control plays a critical role in improving emergency response efficiency and overall urban mobility.
DOI: http://doi.org/10.5281/zenodo.21719253
