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Understanding spatio\u2010temporal distribution of truck dwelling behavior is essential to improve logistics efficiency and urban sustainability. Yet, the freight activity patterns are not well captured by static representation methods. This study introduces a contrastive graph representation learning framework to characterize the spatio\u2010temporal and behavioral features of truck dwelling locations using large\u2010scale GPS trajectory data. We construct a time\u2010varying directed truck flow network. Building on edge convolution, we develop a modified Edge Convolution Network (ECN) with attention\u2010based aggregation to learn spatial dependencies and temporal dynamics. These embeddings are used to delineate logistics activity zones through unsupervised clustering. The framework is tested and evaluated with a massive truck trajectory dataset and compared with other baseline methods. The results prove that the modified ECN model achieves better performance than baselines and reveals interpretable spatio\u2010temporal activity patterns providing valuable insights in logistics amelioration.<\/jats:p>","DOI":"10.1111\/tgis.70292","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:04:21Z","timestamp":1780315461000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Spatio\u2010Temporal Heterogeneity of Urban Truck Dwelling Behavior With a Contrastive Graph Representation Framework"],"prefix":"10.1111","volume":"30","author":[{"given":"Ziyi","family":"Wang","sequence":"first","affiliation":[{"name":"College of Urban Transportation and Logistics &amp; Center for Spatio\u2010Temporal Intelligence Shenzhen Technology University  Shenzhen PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongxi","family":"Gong","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of Urban Planning and Decision Making Harbin Institute of Technology, Shenzhen  Shenzhen PR China"},{"name":"School of Architecture Harbin Institute of Technology, Shenzhen  Shenzhen PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christophe","family":"Claramunt","sequence":"additional","affiliation":[{"name":"Naval Academy Research Institute  Lanv\u00e9oc France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9127-8793","authenticated-orcid":false,"given":"Meihan","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Urban Transportation and Logistics &amp; Center for Spatio\u2010Temporal Intelligence Shenzhen Technology University  Shenzhen PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Tu","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo\u2010Environmental Monitoring of Great Bay Area Shenzhen University  Shenzhen PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.10.013"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijtst.2021.07.001"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2012.06.010"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03285-x"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/bse.4114"},{"issue":"3","key":"e_1_2_10_7_1","first-page":"670","article-title":"Forecasting Sparse Movement Speed of Urban Road Networks With Nonstationary Temporal Matrix Factorization","volume":"59","author":"Chen X.","year":"2025","journal-title":"Transportation Science"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.3141\/2478-02"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12544-020-00430-w"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranpol.2022.01.016"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.01.002"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12544-018-0341-5"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2021.101619"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2022.11.021"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/10095020.2025.2483888"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trip.2025.101482"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rtbm.2024.101177"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/19475683.2025.2552157"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2024.103822"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.3141\/2596-06"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijtst.2024.04.007"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.127252"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2016.06.035"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/int.22443"},{"key":"e_1_2_10_25_1","unstructured":"Veli\u010dkovi\u0107 P. 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