Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.

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 helped develop TranSEC. «And unlike other models that only work in one specific metro area, our tool is portable and can be applied to any urban area where aggregated traffic data is available.»

UBER-fast traffic analysis

TranSEC (which stands for transportation state estimation capability) differentiates itself from other traffic monitoring methods by its ability to analyze sparse and incomplete information. It uses machine learning to connect segments with missing data, and that allows it to make near real-time street level estimations.

Story Source: Materials provided by DOE/Pacific Northwest National Laboratory. Original written by Karyn Hede. Note: Content may be edited for style and length.