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Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational control. This paper presents a shared latent-space framework that connects simulator calibration and reinforcement learning control through a common learned representation of urban traffic dynamics. First, we develop a combinatorial MLP-autoencoder architecture that learns low-dimensional manifolds linking simulator inputs (origin-destination demand, network parameters) to outputs (travel times, congestion patterns), enabling efficient Bayesian optimi

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