The Need

Urban traffic accidents are difficult to predict because risk changes rapidly with traffic conditions, weather, human activity, road characteristics, and events occurring elsewhere in a city. Existing prediction approaches often rely on fixed historical windows or isolated data sources, limiting their ability to respond effectively to changing conditions and complex spatial relationships. Cities, mobility providers, and transportation operators need more accurate, scalable, and trustworthy forecasts to anticipate elevated accident risk and make proactive safety decisions.

The Technology

OSU engineers have developed an adaptive, multimodal AI framework for forecasting traffic accident risk across urban environments. The technology integrates diverse sources of temporal, spatial, environmental, and geographic information while dynamically adjusting how much historical information is considered based on changing conditions. It evaluates both localized and broader citywide influences to generate location-specific accident-risk forecasts. The framework also quantifies prediction confidence, enabling risk outputs to be incorporated into operational dashboards, alerts, and decision-support systems.

Commercial Applications

• Smart-city and traffic management platforms: Predictive safety monitoring and proactive traffic interventions.
• Navigation, mobility, and rideshare: Risk-aware routing and fleet management.
• Insurance and logistics: Location- and time-specific risk assessment and operational planning.
• Emergency response: Data-driven deployment of response resources during elevated-risk periods.

Benefits/Advantages

• Adaptive forecasting: Adjusts the historical context used for predictions as transportation conditions change.
• More comprehensive risk assessment: Combines multiple data types and considers both local and citywide influences.
• Scalable performance: Designed to support efficient prediction across large urban environments.
• Decision-ready outputs: Provides risk probabilities and calibrated confidence estimates to support operational decision-making and resource allocation.