城市交通网络在实现高保真模拟器校准和实时运行控制方面面临着复杂的优化挑战。本文提出了一种共享潜在空间框架，通过城市交通动力学的共同学习表征，将模拟器校准与强化学习控制联系起来。首先，我们开发了一种组合式 MLP-自编码器架构，该架构学习连接模拟器输入（起讫点需求、网络参数）与输出（旅行时间、拥堵模式）的低维流形，从而支持高效的贝叶斯优化以进行校准。与传统降维方法相比，该方法展现出更优的样本效率，并在固定计算预算内实现了对观测数据的更好拟合。其次，我们实施了一种带有经验回放和目标网络的深度 Q-learning 智能体，通过调度与路径调整来优化动态交通分配。在基准网络上的实证评估显示，与基线运营相比，我们的方法可将系统总旅行时间减少高达 51%。所学习的潜在表征不仅用于降低贝叶斯校准的维度，还被纳入强化学习的状态表征中，使控制策略能够在压缩且经过校准的交通动力学上运行。这种共享潜在空间的公式化为智能交通系统中从模拟器校准到自适应运行控制提供了统一的路径。研究结果凸显了深度学习方法在城市出行规划与管理中的变革潜力，特别是在传统优化方法面临计算瓶颈的大规模网络中。

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.