Optimal Urban Emergency Routing Using Real-Time Audio Recognition and Graph Theory-Based Path Planning

Emergency vehicle routing during a crisis in urban areas is a high-priority task that requires optimal routes for immediate crisis aversion and management. These dire straits may include fire accidents, medical emergencies, and many other unpredictable situations. An approach that provides fast, resource-efficient, and optimized routes is desired in such situations. This study proposes a cost-effective method that addresses these key objectives. Python is chosen as a primary language due to its cost-effectiveness and versatility. The Librosa library plays a vital role in sound detection and recognition on a real-time basis. Furthermore, the NetworkX library facilitates the design and planning of the optimized routes and uses graph theory to achieve the same. The SUMO simulator offers better visualization and evaluation for assessing the effectiveness of the obtained results. The outcome is a flexible and agile system that ensures rapid and efficient response to emergency scenarios.