Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting

Arash Hajisafi, Haowen Lin, Sina Shaham, Haoji Hu, Maria Despoina Siampou, Yao Yi Chiang, Cyrus Shahabi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations

    Abstract

    Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision making in various application domains, from urban planning and transportation management to public health and social studies. Although this forecasting problem can be formulated as a multivariate time-series forecasting task, current approaches cannot fully exploit the ever-changing multi-context correlations among POIs. Therefore, we propose Busyness Graph Neural Network (BysGNN), a temporal graph neural network designed to learn and uncover the underlying multi-context correlations between POIs for accurate visit forecasting. Unlike other approaches where only time-series data is used to learn a dynamic graph, BysGNN utilizes all contextual information and time-series data to learn an accurate dynamic graph representation. By incorporating all contextual, temporal, and spatial signals, we observe a significant improvement in our forecasting accuracy over state-of-the-art forecasting models in our experiments with real-world datasets across the United States.

    Original languageEnglish (US)
    Title of host publication31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
    EditorsMaria Luisa Damiani, Matthias Renz, Ahmed Eldawy, Peer Kroger, Mario A. Nascimento
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9798400701689
    DOIs
    StatePublished - Nov 13 2023
    Event31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023 - Hamburg, Germany
    Duration: Nov 13 2023Nov 16 2023

    Publication series

    NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

    Conference

    Conference31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
    Country/TerritoryGermany
    CityHamburg
    Period11/13/2311/16/23

    Bibliographical note

    Publisher Copyright:
    © 2023 Owner/Author(s).

    Keywords

    • POI visiting patterns
    • graph neural networks
    • multi-context correlations
    • time-series forecasting

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