Unjam Traffic Gym
Project Overview
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About
With 68% of the world’s population expected to live in cities by 2050, traffic congestion will become a major issue, especially in developing nations. The Unjam Traffic Gym addresses this by creating high-fidelity digital twins of cities, incorporating real traffic data. This tool allows municipalities to safely simulate and validate infrastructure changes or traffic strategies before implementation. It bridges the gap between research and practice, helping to reduce CO2 emissions, pollution, and economic losses from congestion
Team
Alessio Rimoldi
ETH Zurich
Alessio is a PhD Candidate at ETH Zürich working at the intersection of Optimal Control and Reinforcement Learning. He completed his Master's degree in applied mathematics in 2023 after working for two years as a Machine Learning Engineer at a tech company. He's also currently CTO of the Unjam project, born at ETH Zürich as part of his Master's thesis aimed at optimizing urban mobility through digital twins.
Carlo Cenedese
ETH Zurich
I am an Assistant Professor at TU Delft and a Senior Scientist at ETH Zürich, I focus on game theory, distributed optimization, and smart infrastructure systems. Co-founder of "unjam," which develops IDE development for digital twins.
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