Think of a city’s road network as a giant, messy orchestra. This use case is about putting an AI ‘conductor’ in charge that can see what’s happening on the roads in real time (via cameras and sensors), predict where jams and accidents might happen, and then adjust traffic lights, signals, and routing instructions to keep everything flowing smoothly.
Urban traffic is increasingly congested, unsafe, and inefficient. Traditional traffic control systems are mostly static and reactive; they can’t fully leverage real‑time data from cameras, sensors, and connected vehicles. This use case applies AI to perceive traffic conditions, optimize signal timing, coordinate intersections, and support better operational decisions, reducing congestion, travel time, fuel consumption, and accidents.
Proprietary access to city-scale traffic sensor data and historical flows, long-term integration contracts with municipalities, and deep integration with existing traffic control hardware and ITS backbones create switching costs and operational stickiness.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Real-time ingestion and processing of large volumes of heterogeneous sensor data (video, loop detectors, GPS) with low latency and high reliability across a city-scale deployment.
Early Majority
Focus on combining intelligent perception (e.g., camera/sensor understanding) with real-time operational optimization and control, moving beyond static timing plans to adaptive, learning-based city-wide traffic management.