Conducts data-driven research that develops and employs sophisticated algorithms, machine learning and statistical methods, innovative data fusion, and visualization techniques to advance transportation.
The Division of Data & Analytics (DDA) specializes in collaboration with industry, academic, and government partners to translate large-scale data collections into robust and timely guidance and decisions. The division focuses on challenging questions at the intersection of mechanical engineering, physics, computer science, statistics, behavior, performance, safety and policy. DDA projects leverage innovative data fusion approaches, algorithmic labeling processes, and interactive visualizations to translate disparate and highly dimensional data into visible progress and understandable results. The division's goals are to provide domain expertise and state-of-the-art data and analytic methods to enable our partners to answer their questions quickly, cost effectively, and with accessible output that is ready to address their most pressing needs.
How will occupantless vehicles – those with no driver or passengers onboard – used for delivery goods to consumers influence crash risk and the resulting injuries and fatalities? Will they improve road safety?
A cross section of U.S. and Canadian public safety officials (i.e., law enforcement, fire and rescue, and emergency medical personnel) described how they would respond in the most common scenarios involving moving and stationary vehicles, and then asked how the presence of a vehicle equipped with an automated driving system might alter their set procedures.
The partner-driven AMP program provides industry members with access to real-world driving data as well as tools that are focused on the development and evaluation of automated driving technologies.