A new machine learning algorithm is poised to help urban transportation analysts relieve bottlenecks and chokepoints that routinely snarl city traffic.
The tool, called TranSEC, was developed at the U.S. Department of Energy’s Pacific Northwest National Laboratory 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 a national laboratory.
“What’s novel here is the street level estimation over a large metropolitan area,” said Arif Khan, a PNNL computer scientist who