AI Urban Congestion Intelligence
AI Urban Congestion Intelligence uses real-time data from cameras, sensors, and connected infrastructure to detect, predict, and alleviate traffic congestion across city road networks. It dynamically optimizes signal timing, incident response, and routing to improve travel times, reduce emissions, and enhance road safety. This enables public agencies to maximize existing infrastructure capacity and deliver more reliable mobility without costly new construction.
The Problem
“Real-time congestion detection, forecasting, and signal optimization across a city network”
Organizations face these key challenges:
Operators discover congestion too late (manual camera wall monitoring, delayed incident reports)
Signal timing updates are slow, inconsistent, and not coordinated across corridors
No reliable short-horizon forecasts (15–60 min) to proactively manage events, weather, or peaks
Hard to quantify impact (before/after travel time, queue length, safety hotspots) for public reporting