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Research Highlights

Vehicle Sensor Data (VSD) Based Traffic Control in Connected Automated Vehicle (CAV) Environment

Xianfeng (Terry) Yang
It can be expected that automated vehicles and human-driven vehicles will coexist in the transportation network for quite some time. In order to support various traffic control tasks it is critical to develop a reliable model to understand the real-time traffic patterns in this mixed environment. A new report from the National Institute for Transportation and Communities (NITC) contributes three new tools to help planners model freeway traffic with both connected automated vehicles (CAVs) and... Read More

Evaluating Mobility Impacts Of Construction Workzones On Utah Transportation System Using Machine Learning Techniques

Abbas Rashidi
Roadside construction – be it a detour, a closed lane, or a slow weave past workers and equipment – work zones impact traffic flow and travel times on a system-wide level. The ability to predict exactly what those impacts will be, and plan for them, would be a major help to both transportation agencies and road users. Funded by the National Institute for Transportation and Communities, the latest Small Starts project led by Abbas Rashidi of the University of Utah introduces a robust, deep neu... Read More