Transferable Human Mobility Network Reconstruction with neuroGravity
It addresses the critical data shortage of mobility networks in resource-limited, underdeveloped areas, offering scalable proxies for costly travel surveys.
neuroGravity is a physics-informed deep learning model that reconstructs human mobility networks from limited observations (urban facility and population distributions) and transfers to unobserved cities, generating mobility proxies for over 1,200 cities worldwide.
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.