An automatic-differentiation-based simulator for LTM and DUO traffic models enables scalable gradient optimization of dynamic congestion tolls on networks with thousands of links and millions of vehicles.
A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation
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End-to-end differentiable network traffic simulation with dynamic route choice
An automatic-differentiation-based simulator for LTM and DUO traffic models enables scalable gradient optimization of dynamic congestion tolls on networks with thousands of links and millions of vehicles.