Proactive congestion management via data-driven methods and connected vehicle-based microsimulation

CUTR’s Autonomous-Connected Mobility Evaluation (ACME) program was recently published in the Journal of Intelligent Transportation Systems. In this research, the team proposes a new method that fuses conventional data sources, like radar and loop detectors, with Bluetooth and connected vehicle (CV) probe data. The team produced an algorithm to predict the onset and intensity of recurring and non-recurring congestion (due to weather or crashes). The algorithm can be readily deployed by agencies with currently operational CV infrastructure to better leverage speed harmonization strategies.

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