Emergency Routing Protocol for Intelligent Transportation Systems Using IoT and Generative Artificial Intelligence

Urban populations are always on the increase, increasing the number of vehicles, leading to severe congestion, longer travel times, and substantial emergency vehicle response time delays. This paper presents a routing protocol for Intelligent Transportation Systems (ITS), incorporating a Belief-Desire-Intention (BDI) model and generative Artificial Intelligence (AI) techniques. The proposed system utilizes BDI-based generative AI models, which rely on predefined logic and rules rather than machine learning to make real-time routing decisions. These decisions are based on data from vehicle-to-vehicle (V2V) communications, roadside units (RSUs) and IoT sensors, which integrate traffic density, congestion levels, collision avoidance and hazard detection. The system aims to optimize the accuracy of route selection, energy consumption, and communication latency. The simulation results, implemented using NS3 for real-world traffic scenarios, show improvements in route selection accuracy, collision avoidance, and energy efficiency compared to traditional routing methods. Specifically, the proposed system achieved a route selection accuracy of 95%, a collision avoidance rate of 95%, and reduced the communication latency to 105 ms, outperforming the other two methods. Furthermore, energy consumption was minimized and reduced to 85 J per route. These results highlight the potential of BDI-based routing with generative AI to improve ITS performance, particularly in real-time traffic management.