实时城市治理不仅依赖于知晓人群所在位置，更依赖于掌握其在不同地点之间的移动方式——即具有方向性的流动。此类方向性结构传统上需通过追踪个体在空间中的轨迹来解析，但该方法成本高昂，且所依赖的轨迹数据高度唯一、极易被再识别。本文表明，此类方向性结构无需直接观测即可获知：城市业已采集的聚合计数数据中仍保留了足够信息，可用于重构起讫点（OD）矩阵的时间演化过程。我们采用一种兼顾不确定性的、物理信息驱动的框架，仅利用区域层级的计数数据，在来自美国与中国十二个城市的出行数据集上推断未来OD流，其精度可媲美以历史OD矩阵为输入的模型。概率建模校正了对稀疏但高价值走廊的系统性低估问题，并生成与实际观测流一致的校准化预测。尊重交通规划中“先生成、后分配”逻辑的模型架构能更忠实地复原出行交互，表明应在重建两两交互之前保留位置层级的空间异质性。由于推理阶段仅需训练完成后的聚合观测数据，该方法降低了对持续个体级追踪的依赖，从而为实时城市智能提供了一种更具部署可行性、隐私暴露风险更低的基础。

Real-time urban governance depends not only on knowing where people are, but on how they move between places, directional flows that could be conventionally resolved by tracking individuals through space, i.e., expensive to sustain and built on traces that are highly unique and readily re-identifiable. Here we show that this directional structure need not be observed to be known: aggregated counts which cities already collect retain enough information to reconstruct the temporal evolution of origin-destination (OD) matrix. Using an uncertainty-aware physics-informed framework, we infer future OD flows from area-level counts alone across twelve mobility datasets from cities in the United States and China, reaching accuracy comparable to models that take historical OD matrices as input. Probabilistic modeling corrects the systematic underestimation of sparse, high-value corridors and yields calibrated predictions consistent with observed flows. Architectures that respect the generation-before-assignment logic of transport planning recover interactions more faithfully, indicating that location-level spatial heterogeneity should be preserved before pairwise interactions are reconstructed. Because inference requires only aggregated observations after training, recovering interactions this way reduces reliance on continuous individual-level tracking, pointing toward a more deployable and less exposure-heavy basis for real-time urban intelligence.