MPC Member Publications

This database contains a listing of population studies publications written by MPC Members. Anyone can add a publication by an MPC student, faculty, or staff member to this database; new citations will be reviewed and approved by MPC administrators.

Full Citation

Title: Inferring human movements after snowfall: a weather-informed graph learning model for flow redistribution in mobility networks

Citation Type: Journal Article

Publication Year: 2025

ISSN: 13658824

DOI: 10.1080/13658816.2025.2524394;CTYPE:STRING:JOURNAL

Abstract: Extreme snowfall events significantly affect human dynamics, challenging transportation safety. Current research on inferring mobility patterns post-snowfall is hindered by scale-free network connectivity, abrupt contextual changes, intricate mechanisms of spatial interaction, and multivariate feature handling. This paper proposes the Weather-Informed Mobility Network (WIMN), a graph deep learning model designed to infer the post-snowfall human movement flows within mobility networks. WIMN treats each region of interest as a spatially-embedded graph and has three characteristics: (i) a geo-attraction block informed by the gravity law, recalibrating the spatial-wise importance of locations and interactions; (ii) an Encoder-Decoder structure where Encoder learns stable spatial dependencies and Decoder captures the snow-induced dynamic changes; and (iii) a Pareto-inspired hub selection, processing locations hierarchically according to their degree centrality within the network context. Applied to the Twin Cities Metropolitan Area, Minnesota, USA, WIMN outperformed baseline methods by up to 18.5% in inferring the redistribution of human mobility flows. Additionally, WIMN demonstrated spatial explainability across the learned deep weights, snowfall intensity, and flow reductions. These findings highlight WIMN’s effectiveness in understanding short-term human dynamics after extreme weather events, with potential applications in aiding transportation planning and disaster response.

Url: https://www.tandfonline.com/doi/pdf/10.1080/13658816.2025.2524394

User Submitted?: No

Authors: Wang, Sheng; Zhu, Di

Periodical (Full): International Journal of Geographical Information Science

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