Urban Traffic and Safety Management
Urban Traffic and Safety Management focuses on using data-driven systems to monitor, optimize, and control vehicle and pedestrian movement across city streets and highways while reducing crashes and congestion. It integrates real-time feeds from signals, cameras, sensors, and historical crash and mobility data to continuously adjust traffic operations—such as signal timing, lane use, and routing—and to prioritize infrastructure investments and enforcement. This application matters because traditional traffic engineering relies on infrequent manual studies, static signal plans, and after-the-fact crash analysis, which cannot keep up with growing urban populations, constrained budgets, and safety mandates like Vision Zero. AI enables continuous, citywide visibility and faster detection of bottlenecks and high-risk patterns, helping public agencies improve travel times, reduce fatalities and serious injuries, cut emissions from idling traffic, and deploy limited staff and capital more efficiently.
The Problem
“Real-time congestion and crash-risk control across city streets using multimodal AI”
Organizations face these key challenges:
Signal timing updates rely on periodic studies, not continuous real-world conditions
Slow incident detection and poor situational awareness across corridors and intersections
Crash hot spots are identified after-the-fact, with weak causal evidence for interventions