How does GreenWave AI calculate the optimal route for emergency vehicles in real-time?

[skymehta23-web]

I was looking at the GreenWave AI system and wanted to understand the routing logic. How does it determine the fastest corridor for emergency vehicles? Does it use a standard shortest path algorithm or something custom built for traffic conditions?

[akashcodes23]

I was looking at the GreenWave AI system and wanted to understand the routing logic. How does it determine the fastest corridor for emergency vehicles? Does it use a standard shortest path algorithm or something custom built for traffic conditions?

Great question! GreenWave AI uses a traffic-weighted Dijkstra algorithm — not standard shortest path.
The key difference from vanilla Dijkstra is that edge weights are dynamic, not static. Instead of just distance, each road segment is weighted by:

Real-time traffic density (fetched via Google Maps API)
Signal phase timing (how long until the next green light)
Intersection congestion score (vehicles per minute at junction)

The algorithm runs on a live graph that updates every 500ms via WebSocket — so the corridor recalculates in real-time as traffic conditions change.
python# Simplified weighted edge calculation
def edge_weight(segment):
traffic_factor = get_realtime_density(segment)
signal_delay = get_signal_phase_wait(segment)
return segment.distance * traffic_factor + signal_delay
The AI decision feed then assigns a confidence score to each routing decision — if confidence drops below 0.7, it triggers a reroute. This is what makes GreenWave adaptive rather than just reactive.
The full corridor is pre-signaled ahead of the vehicle using the WebSocket broadcast — all intersections on the calculated route switch to green before the vehicle arrives, not after.