This is like giving city traffic planners a supercharged crystal ball: AI watches patterns from cameras, sensors, and crash data to predict where and when roads are most dangerous, then suggests fixes such as changing signal timing, speed limits, or enforcement focus.
Reduces traffic accidents and fatalities by using AI to spot dangerous patterns in road use faster and more accurately than manual analysis, helping transportation departments prioritize interventions, redesign intersections, and optimize enforcement and emergency response.
Access to city- and state-level traffic, crash, and infrastructure datasets, plus integration into transportation planning and public safety workflows, can become a defensible moat as historical data compounds and models are tuned to local conditions.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Real-time processing of large volumes of video, sensor, and telematics data under public-sector budget and infrastructure constraints, plus data privacy and governance requirements.