How Seattle DOT Uses C3 AI to Cut Collision Analysis Time by 90%

Seattle Department of Transportation (SDOT) is responsible for one of the most complex urban transportation networks in the Pacific Northwest, overseeing more than 4,000 miles of roadway, 7,800 intersections, and $28 billion in city infrastructure serving over 760,000 residents. As travel demand from vehicles, pedestrians, and cyclists continued to grow, so did the city's safety challenges — with more than 6,000 collisions and nearly 30 fatal crashes occurring annually. To address this, SDOT launched Vision Zero, an ambitious citywide initiative targeting the complete elimination of traffic fatalities and serious injuries by 2030.

Despite the urgency of Vision Zero, SDOT's analytical capabilities were not keeping pace with the scale of the problem. Collision analysis depended on manual, repetitive processes that made it difficult to identify high-risk intersections or understand the root causes of severe crashes. Data was scattered across multiple disconnected systems, preventing analysts from gaining a unified, citywide view of roadway safety. Compounding the issue, the department lacked the staffing and in-house expertise needed to process the growing volumes of collision, traffic, and roadway data at its disposal.

Evaluating the impact of past safety investments presented an additional challenge. Intersection upgrades were not consistently tracked or linked to outcome data, leaving planners without reliable evidence of which design treatments or mitigation strategies were actually reducing fatalities. For a data-driven program like Vision Zero, these gaps created real risks: emerging collision patterns could go undetected, efforts could be duplicated across teams, and opportunities for proactive intervention could be missed entirely.

To overcome these barriers, SDOT implemented C3 AI Safety Analysis — a machine learning-powered platform purpose-built for transportation safety. Within just 12 weeks of project kickoff, the C3 AI team integrated five years of historical collision, traffic, and roadway data into a centralized, production-ready application covering all 7,800+ intersections across the city. The platform applies machine learning-based collision severity factor analysis to surface root causes, flag high-risk corridors, and support evidence-based prioritization of safety investments through interactive dashboards.

The results were immediate and measurable. Collision analysis time dropped by more than 90%, enabling near real-time identification of safety hotspots across the city. Engineers and safety planners gained the ability to evaluate the effectiveness of past mitigation measures, track performance trends across corridors, and plan future improvements with far greater precision. With AI-driven insights replacing manual workflows, SDOT is now better positioned to allocate resources toward the highest-impact projects and accelerate meaningful progress toward its Vision Zero goal.