Predictive traffic tools to guide AVs and slash congestion

Megan Lampinen speaks to the Pacific Northwest National Laboratory about its new machine learning algorithm, TranSEC

The number of mobility options on offer to city residents and visitors has been growing rapidly, but urban congestion remains a serious problem. It not only exacerbates air pollution and wastes time, but it is also a tremendous drag on local economies. Traffic consultancy INRIX estimates that congestion can cost cities tens of billions of dollars a year.

A new machine learning algorithm could help with that. The US Department of Energy’s Pacific Northwest National Laboratory (PNNL) has developed a tool, called TranSEC (Transportation State Estimation Capability), to provide urban traffic engineers with actionable information about traffic patterns in a given city. While other tools on the market can optimise an individual traveller’s journey from A to B, city traffic engineers want to ensure all vehicles reach their destinations efficiently. There may be times when a route that is efficient for an individual driver results in too many vehicles accessing a road that was never designed to handle high volumes of traffic….

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