Machine Learning Algorithms Help Predict Traffic Headaches

Arterial streets surrounding the I-210 freeway in southern California, where the first traffic prediction pilot is taking place. Urban traffic roughly follows a periodic pattern associated with the typical "9 to 5" work schedule. However, when an accident happens, traffic patterns are disrupted. Designing accurate traffic flow models, for use during accidents, is a major challenge for traffic engineers, who must adapt to unforeseen traffic scenarios in real time. A team of Lawrence Berkeley National Laboratory (Berkeley Lab) computer scientists is working with the California Department of Transportation (Caltrans) to use high performance computing (HPC) and machine learning to help improve Caltrans' real-time decision making when incidents occur.