发表机构
Peking University; Agency for Science, Technology and Research (A*STAR); Institute of High-Performance Computing; Hong Kong University of Science and Technology; Fuzhou University（北京大学; 新加坡科技研究局; 高性能计算研究院; 香港科技大学; 福州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种不确定性感知的物理信息框架，仅利用城市已有的聚合计数重建并预测OD矩阵，在美中十二个数据集上达到与历史OD输入相当的精度，减少对个体追踪的依赖。
AI 中文摘要
实时城市治理不仅取决于知道人们在哪里，还取决于他们如何在地方之间移动，即方向性流动，这种流动传统上可以通过追踪个体在空间中的移动来解决，但这种方式成本高昂且依赖于高度独特且易于重新识别的轨迹数据。在此，我们表明这种方向性结构无需被观测即可得知：城市已收集的聚合计数保留了足够的信息，以重建起点-终点（OD）矩阵的时间演化。利用一个不确定性感知的物理信息框架，我们仅从区域级计数推断未来的OD流量，覆盖来自美国和中国的十二个城市移动数据集，达到了与以历史OD矩阵为输入的模型相当的精度。概率建模纠正了对稀疏、高价值走廊的系统性低估，并产生与观测流量一致的校准预测。尊重交通规划中“先生成后分配”逻辑的架构能更忠实地恢复交互，表明在重建成对交互之前应保留位置级的空间异质性。由于推理在训练后仅需聚合观测数据，这种方式恢复交互减少了对连续个体级追踪的依赖，为实时城市智能提供了更易部署且暴露风险更低的基石。
英文摘要
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.
