深度元模型与强化学习：城市交通网络校准与控制的统一框架
Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks
交通仿真器校准和深度 Q-learning 控制共用一套潜空间，基准网络上行程时间最多降 51%，做城市交通优化可以看看。
论文提出一个共享潜空间框架，把交通仿真器校准与强化学习控制接到同一套学习表征上。组合式 MLP-autoencoder 学习从起点-终点需求、网络参数到行程时间的低维流形，使贝叶斯优化校准在固定算力预算下比传统降维方法样本效率更高。控制端用带经验回放和目标网络的 deep Q-learning 智能体，通过调度和路由调整优化动态交通分配。在基准网络实测中，该方法把系统级行程时间相比基线最多降低 51%。
Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks
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 optimization for calibration. This approach demonstrates superior sample efficiency compared to traditional dimension reduction methods, achieving better fit to observational data within fixed computational budgets. Second, we implement a deep Q-learning agent with experience replay and target networks to optimize dynamic traffic assignment through scheduling and routing adjustments. In empirical evaluations on benchmark networks, our approach reduces system-wide travel times by up to 51% compared to baseline operations. The learned latent representation is not only used to reduce the dimensionality of Bayesian calibration, but is also incorporated into the reinforcement learning state representation, allowing the control policy to operate on compressed and calibrated traffic dynamics. This shared latent-space formulation provides a unified pathway from simulator calibration to adaptive operational control within intelligent transportation systems. Our results highlight the transformative potential of deep learning methods in urban mobility planning and management, particularly for large-scale networks where traditional optimization approaches face computational bottlenecks.