Project story
From question to working system.
This multi-part research effort investigated how artificial intelligence, machine learning, and emerging transportation datasets can strengthen conventional highway-safety analysis and help agencies move beyond an exclusive reliance on historical crashes.
The guide organizes six applied research studies spanning connected-vehicle turning movements, street-level imagery, LiDAR roadway inventories, proactive conflicts, road-user behavior, and other safety applications. The work links data-source characteristics and validation choices to questions practitioners actually face.
My contributions included model development, coding, validation, technical documentation, user guidance, reporting, and engagement with federal and state stakeholders—connecting methodological experimentation with implementation needs.
