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This project deals with real time traffic control and re-routing of paths for 911 dispatchers and emergency services.
Inspiration
During emergencies, every second counts , yet ambulances and fire trucks often face delays due to traffic congestion and poor route coordination. We wanted to build a system that gives authorized responders smarter navigation tools and control over their routes, reducing risk and saving lives.
What it does
RescueRoute AI continuously scans nearby roads for ambulances and fire trucks, identifies the fastest and safest paths, and alerts dispatchers to hazards like accidents or construction. When traffic prevents a clear route, authorized units can request temporary signal overrides, turning lights green along their emergency path for rapid, safe passage.
How we built it
We built the prototype using Node.js, React, and real-time geolocation APIs along with Figma Make to simulate live emergency unit tracking. The system’s AI logic prioritizes routes by traffic density, hazard reports, and distance to hospitals. The dashboard interface was designed in Figma and integrated with mock data to visualize live status updates and signal changes.
Challenges we ran into
Integrating live location updates smoothly with map visualization.
Simulating realistic emergency routing logic without real-time city traffic data.
Managing permission control for authorized override requests.
Accomplishments that we're proud of
Built a functional prototype that visualizes emergency units, hazards, and traffic signals in real time.
Designed a clean, responsive dashboard for dispatch monitoring.
Developed an AI-based logic to dynamically optimize routes under pressure.
What we learned
We learned how to integrate real-time systems, design user-centered dashboards, and balance safety with efficiency in smart transportation. We also gained insight into the ethical and technical challenges of implementing AI in emergency services.
What's next for RescueRouteAI
We plan to integrate live city traffic APIs, expand to multi-district coordination, and collaborate with local governments to test real-world implementation. Future versions will include predictive routing and automated traffic light synchronization through IoT integration.
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