Detecting urban mobility patterns is crucial for policymakers in urban and transport planning. Mobile phone data have been increasingly deployed to measure the spatiotemporal variations in human mobility. This work applied non-negative Tucker decomposition (NTD) to mobile phone-based origin–destination (O-D) matrices to explore mobility patterns’ latent spatial and temporal relationships in the province of Liège, Belgium. Four [Formula: see text] traffic tensors have been built for one regular weekday, one regular weekend day, one holiday weekday, and one holiday weekend day, respectively. The proposed method inferred spatial clusters and temporal patterns while interpreting the correlation between spatial clusters and temporal patterns through geographical visualization. As a result, we found the similarity of O-D and destination–origin (D-O) patterns and the symmetry for the trips of the temporal patterns with evening peak and morning peaks on the weekday. Moreover, we investigated the attraction of different spatial clusters with two temporal patterns on a regular weekday and validated the reconstructed demand using population counts and commuting matrices. Finally, the differences in spatial and temporal interactions have been addressed in detail.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Tensor Decomposition for Spatiotemporal Mobility Pattern Learning with Mobile Phone Data


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Gong, Suxia (Autor:in) / Saadi, Ismaïl (Autor:in) / Teller, Jacques (Autor:in) / Cools, Mario (Autor:in)


    Erscheinungsdatum :

    2024-08-08




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Nonnegative tensor decomposition for urban mobility analysis and applications with mobile phone data

    Wang, Dianhai / Cai, Zhengyi / Cui, Yanlei et al. | Taylor & Francis Verlag | 2022


    Comprehensive Review of Travel Behavior and Mobility Pattern Studies That Used Mobile Phone Data

    Rojas, Mario B. / Sadeghvaziri, Eazaz / Jin, Xia | Transportation Research Record | 2016



    Scalable low-rank tensor learning for spatiotemporal traffic data imputation

    Chen, Xinyu / Chen, Yixian / Saunier, Nicolas et al. | Elsevier | 2021


    Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

    Chao Yu / Haiying Li / Xinyue Xu et al. | DOAJ | 2021

    Freier Zugriff