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Complexity Scientist Beats Traffic Jams Through Adaptation | Quanta Magazine

Complexity Scientist Beats Traffic Jams Through Adaptation

To tame urban traffic, the computer scientist Carlos Gershenson finds that letting transportation systems adapt and self-organize often works better than trying to predict and control them.

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Rodrigo Pérez OrtegaWriting Intern

September 28, 2020

Mexico City is famous for its museums, food and culture, but also for its traffic jams. The city has a population of close to 22 million people and more than 6 million cars, and two-hour daily commutes to school or work are the rule for many people. Perhaps because delays are routine, it’s often socially acceptable to be 10 to 15 minutes late to classes or meetings.

How people travel in the Mexican capital is a complex problem that cannot be reduced to just one or two variables, and it is emblematic of the urban mobility challenges facing half of the world’s population. It’s also the kind of problem that for the past two decades has been a favorite of Carlos Gershenson, a computer scientist at the National Autonomous University of Mexico who is affiliated with both its Institute for Research in Applied Mathematics and Systems and its Center for Complexity Sciences.

To solve a complex problem, Gershenson believes, scientists need to let go of traditional methods and find novel ways to study ever-changing challenges. “Science and engineering have assumed that the world is predictable, and that we just need to find the proper laws of nature to be able to foresee the future,” he wrote while he was a visiting professor at the Massachusetts Institute of Technology and Northeastern University in 2016. “But the study of complex systems has shown that this assumption is misguided.”

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Complexity Scientist Beats Traffic Jams Through Adaptation | Quanta Magazine