Over the course of 2020, when car trips were at a minimum due to the pandemic, Americans still spent a day of their lives, on average, sitting in traffic. Things were much worse pre-pandemic: In 2019, the average driver in Los Angeles spent almost 120 hours in traffic—nearly an entire work week. Traffic is one of the foremost inconveniences of modern life; it’s also unhealthy, inefficient and wasteful, which is why technologists are leveraging the power of data and machine learning to beat back it back.
On today’s Brainstorm, Michal Lev-Ram and Brian O’Keefe explore the technology behind solving system-wide traffic and mobility issues.
Tiffany Chu, SVP of Remix at Via, a mobility platform, explains how COVID has forced cities to quickly rethink the best ways for people to get around.
Jibo Sanyal, a computer scientist with Oak Ridge National Laboratory in Tennessee, discusses his work with the city of Chattanooga to create a “digital twin” of the city, thanks to its traffic sensors, enabling them to find solutions to traffic problems virtually, before implementing them in real life.
Also in this episode, Karina Ricks, Director of the city of Pittsburgh’s Department of Mobility and Infrastructure explains where the city is using adaptive traffic lights, which sense the traffic demand and time the lights accordingly, and where it’s not.
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Can Tech Fix Our Traffic Woes? | Brainstorm podcast - Listen or read transcript on Metacast
