A team from the Lawrence Berkeley National Lab at the University of California (UC) is working with the California Department of Transportation (Caltrans) to use high performance computing (HPC) and machine learning to help improve decision making in real time from the agency when traffic incidents occur. .

City traffic roughly follows a periodic pattern associated with the typical “9 to 5” workweek schedule. However, when an accident does occur, traffic patterns are disrupted. Designing accurate traffic flow models for use during incidents is a major challenge for traffic engineers, who must adapt to unforeseen traffic scenarios in real time. The new research was conducted in collaboration with California Partners for Advanced Transportation Technology (PATH), part of UC Berkeley’s Institute for Transportation Studies (ITS), and Connected Corridors, a collaborative program to research, develop and testing an integrated corridor management approach to controlling traffic on major roads in California.

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