Aggregated city data reveals how people move without tracking individuals
Inferring Urban Mobility Interactions from Aggregated Dynamics
Machine Learning
Summary
Knowing how people move around a city helps manage it better, but tracking each person can be expensive and risky for privacy. The authors show that it is possible to figure out how people travel between places by using only overall counts of where people are, without following anyone individually. They use a special model that includes uncertainty and physical knowledge to predict these travel patterns in different cities. This approach works almost as well as methods that need detailed tracking and could lead to safer, simpler ways to understand city movement in real time.
urban mobilityorigin-destination matrixaggregated dataprivacyprobabilistic modelingreal-time governancespatial heterogeneitytransport planninguncertainty-awaretraffic flow
Authors
Yi Wang, Jing Li, Jinliang Deng, Zhenghong Wang, Yizhi Zhang, Fan Zhang, Ivor W. Tsang, Yu Liu
Abstract
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.