用有限数据重建全球1200+城市人流网络，还能跨城迁移。
Transferable Human Mobility Network Reconstruction with neuroGravity
- 基于设施和人口分布，用物理启发的深度学习重建人流。
- 仅凭少量数据就预测出超1200个城市的人流模式。
- 发现收入空间分化程度决定模型能否跨城迁移。
准确建模人类流动对城市规划与公共卫生至关重要。在缺乏全面出行调查的欠发达地区，需从公开数据重建流动网络。本文提出neuroGravity——一种融合物理规律的深度学习模型，能从有限观测中可靠重构流动，并实现跨城市迁移。仅使用城市设施与人口分布数据，发现neuroGravity的区域表征与社会经济及宜居水平高度相关，可作为昂贵调查的可扩展替代方案。进一步发现，空间收入隔离是模型迁移性的关键：当目标城市与源城市隔离程度相似时，流动网络重建最可靠。为此设计隔离指数，可准确预测迁移性能。最终生成全球1200多个城市的流动代理数据，凸显其缓解资源匮乏地区数据短缺的巨大潜力。
原文摘要 · Abstract (English)
Accurate modeling of human mobility is critical for tackling urban planning and public health challenges. In undeveloped regions, the absence of comprehensive travel surveys necessitates reconstructing mobility networks from publicly available data. Here we develop neuroGravity, a physics-informed deep learning model that reliably reconstructs mobility flows from limited observations and transfers to unobserved cities. Using only urban facility and population distributions, we find that neuroGravity's regional representations strongly correlate with socioeconomic and livability status, offering scalable proxies for costly surveys. Furthermore, we uncover that spatial income segregation plays a key role in model transferability: mobility networks are most reliably reconstructed when target cities share similar segregation levels with the source. We design an index to quantify this segregation and accurately predict transferability. Finally, we generate mobility flow proxies for over 1,200 cities worldwide, highlighting neuroGravity's potential to mitigate critical data shortages in resource-limited, underdeveloped areas.
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