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PNNL TranSEC tool uses UBER data to track and potentially alleviate urban traffic congestion Researchers at Pacific Northwest National Laboratory (PNNL) have developed a new machine-learning-based tool to help urban transportation analysts relieve bottlenecks and chokepoints that routinely snarl city traffic. The tool Transportation State Estimation Capability (TranSEC) was developed to help urban traffic engineers get access to actionable information about traffic patterns in their cities. Currently, publicly available traffic information at the street level is sparse and incomplete. Traffic engineers generally have relied on isolated traffic counts, collision statistics and speed data to determine roadway conditions. The new tool uses traffic datasets collected from UBER drivers and other publicly available traffic sensor data to map street-level traffic flow over time. It creates a big picture of city traffic using machine learning tools and the computing resources available at

Transec los angelesLos angelesLos angeles metroArun sathanurArif khanRobert ralloKatherine wolfArun sathanur pacific northwest national laboratoryEnergy efficient mobility systems programResearchers at pacific northwest national laboratoryRenewable energy vehicle technologies officePacific northwest national laboratoryTransportation state estimation capabilityLos angeles metro areaUrban computing workshopKnowledge discovery