Real-time risk and crash-type prediction
Connected-vehicle trajectories and risky-driving events offer information beyond fixed traffic detectors. My work links these observations with traffic states to predict both crash occurrence and crash type.
The published BiLSTM-Transformer study examines spatiotemporal prediction using connected-vehicle data. Machine learning with causal mediation provides a complementary way to investigate the pathways behind estimated risk.
Behavior-aware prevention
Current work examines driver-group differences, perception, and interactions between roadway environment and driving behavior. The submitted BusMEP manuscript addresses bus safety-risk prediction; it is not a study of autonomous buses.
A separate submitted deep-ensemble and causal-learning study focuses on multi-source freeway safety assessment.
Safety and operations
Speed-management measures of effectiveness connect risk evidence with operational decisions. My FDOT PI proposal is pending approval, while related published studies examine tunnel crash severity and congestion duration and post-crash speed forecasting.
Methods: spatiotemporal sequence learning, causal mediation, interpretable models, and multi-source data integration.