{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T03:06:40Z","timestamp":1784084800557,"version":"3.55.0"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["8252005"],"award-info":[{"award-number":["8252005"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52402377"],"award-info":[{"award-number":["52402377"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52572335"],"award-info":[{"award-number":["52572335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115601","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T11:55:38Z","timestamp":1783684538000},"page":"115601","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P5","title":["Adaptive hypergraph clustering of trucks based on high-order mobility correlations"],"prefix":"10.1016","volume":"181","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1894-1912","authenticated-orcid":false,"given":"Xia","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5810-6270","authenticated-orcid":false,"given":"Menglin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6802-6731","authenticated-orcid":false,"given":"Zhihong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoya","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Zong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.engappai.2026.115601_b1","first-page":"12","article-title":"Incorporating k-means, hierarchical clustering and pca in customer segmentation","volume":"3","author":"Abdulhafedh","year":"2021","journal-title":"J. City Dev."},{"key":"10.1016\/j.engappai.2026.115601_b2","doi-asserted-by":"crossref","unstructured":"Bo, D., Wang, X., Shi, C., Zhu, M., Lu, E., Cui, P., 2020. Structural deep clustering network. In: Proceedings of the Web Conference 2020. pp. 1400\u20131410.","DOI":"10.1145\/3366423.3380214"},{"key":"10.1016\/j.engappai.2026.115601_b3","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.trc.2017.03.021","article-title":"Analyzing year-to-year changes in public transport passenger behaviour using smart card data","volume":"79","author":"Briand","year":"2017","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"issue":"301","key":"10.1016\/j.engappai.2026.115601_b4","first-page":"1","article-title":"Theoretical foundations of t-sne for visualizing high-dimensional clustered data","volume":"23","author":"Cai","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.engappai.2026.115601_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110292","article-title":"Hypergraph modeling and hypergraph multi-view attention neural network for link prediction","volume":"149","author":"Chai","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.115601_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111861","article-title":"Neighborhood convolutional graph neural network","volume":"295","author":"Chen","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.115601_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.113369","article-title":"Mamba-CorRL: Mamba-correlation graph convolutional networks with reinforcement learning for traffic flow prediction","volume":"165","author":"Chen","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115601_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107624","article-title":"BLOCK-DBSCAN: Fast clustering for large scale data","volume":"109","author":"Chen","year":"2021","journal-title":"Pattern Recognit."},{"issue":"6","key":"10.1016\/j.engappai.2026.115601_b9","doi-asserted-by":"crossref","first-page":"3939","DOI":"10.1109\/TSMC.2019.2956527","article-title":"KNN-BLOCK DBSCAN: Fast clustering for large-scale data","volume":"51","author":"Chen","year":"2019","journal-title":"IEEE Trans. Syst. Man Cybern.: Syst."},{"key":"10.1016\/j.engappai.2026.115601_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110112","article-title":"Hypergraph temporal multi-behavior recommendation","volume":"145","author":"Choi","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115601_b11","series-title":"Pacific Rim International Conference on Artificial Intelligence","first-page":"29","article-title":"Unsupervised clustering using a variational autoencoder with constrained mixtures for posterior and prior","author":"Chowdhury","year":"2024"},{"key":"10.1016\/j.engappai.2026.115601_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2021.126058","article-title":"Assessing temporal\u2013spatial characteristics of urban travel behaviors from multiday smart-card data","volume":"576","author":"Deng","year":"2021","journal-title":"Phys. A"},{"key":"10.1016\/j.engappai.2026.115601_b13","first-page":"1","article-title":"Classification via structure-preserved hypergraph convolution network for hyperspectral image","volume":"61","author":"Duan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.115601_b14","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"vol. 96, no. 34","author":"Ester","year":"1996"},{"key":"10.1016\/j.engappai.2026.115601_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2022.127500","article-title":"Choices of intercity multimodal passenger travel modes","volume":"600","author":"Feng","year":"2022","journal-title":"Phys. A"},{"key":"10.1016\/j.engappai.2026.115601_b16","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.trc.2019.04.020","article-title":"Whereabouts of truckers: An empirical study of predictability","volume":"104","author":"Gan","year":"2019","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"issue":"1","key":"10.1016\/j.engappai.2026.115601_b17","first-page":"100","article-title":"Algorithm AS 136: A k-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Soc. Ser. C. Appl. Stat."},{"issue":"3","key":"10.1016\/j.engappai.2026.115601_b18","first-page":"2231","article-title":"Adaptive hypergraph auto-encoder for relational data clustering","volume":"35","author":"Hu","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"10.1016\/j.engappai.2026.115601_b19","doi-asserted-by":"crossref","first-page":"5454","DOI":"10.1109\/TVT.2023.3333848","article-title":"A GNN-enabled multipath routing algorithm for spatial-temporal varying LEO satellite networks","volume":"73","author":"Huang","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"4","key":"10.1016\/j.engappai.2026.115601_b20","doi-asserted-by":"crossref","first-page":"3471","DOI":"10.1109\/TKDE.2021.3125020","article-title":"CaEGCN: Cross-attention fusion based enhanced graph convolutional network for clustering","volume":"35","author":"Huo","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"10.1016\/j.engappai.2026.115601_b21","first-page":"76","article-title":"Understanding public transit patterns with open geodemographics to facilitate public transport planning","volume":"16","author":"Liu","year":"2020","journal-title":"Transp. A: Transp. Sci."},{"key":"10.1016\/j.engappai.2026.115601_b22","first-page":"7603","article-title":"Deep graph clustering via dual correlation reduction","volume":"vol. 36, no. 7","author":"Liu","year":"2022"},{"issue":"4","key":"10.1016\/j.engappai.2026.115601_b23","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1287\/msom.2019.0776","article-title":"Shipping to heterogeneous customers with competing carriers","volume":"22","author":"Lu","year":"2020","journal-title":"Manuf. Serv. Oper. Manag."},{"issue":"5","key":"10.1016\/j.engappai.2026.115601_b24","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1080\/13658816.2024.2389410","article-title":"Quantifying local mobility patterns in urban human mobility data","volume":"39","author":"Malekzadeh","year":"2025","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.engappai.2026.115601_b25","doi-asserted-by":"crossref","unstructured":"Peng, Z., Liu, H., Jia, Y., Hou, J., 2021. Attention-driven graph clustering network. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 935\u2013943.","DOI":"10.1145\/3474085.3475276"},{"key":"10.1016\/j.engappai.2026.115601_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112922","article-title":"Hypergraph neural network with state space models for node classification","volume":"163","author":"Quadir","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115601_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.107954","article-title":"A vehicle value based ride-hailing order matching and dispatching algorithm","volume":"132","author":"Shi","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115601_b28","doi-asserted-by":"crossref","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","article-title":"Unsupervised K-means clustering algorithm","volume":"8","author":"Sinaga","year":"2020","journal-title":"IEEE Access"},{"issue":"11","key":"10.1016\/j.engappai.2026.115601_b29","doi-asserted-by":"crossref","first-page":"11860","DOI":"10.1109\/TKDE.2023.3235312","article-title":"Self-supervised hypergraph representation learning for sociological analysis","volume":"35","author":"Sun","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.engappai.2026.115601_b30","doi-asserted-by":"crossref","unstructured":"Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., Zhang, C., 2019. Attributed Graph Clustering: a Deep Attentional Embedding approach. In: International Joint Conference on Artificial Intelligence 2019. pp. 3670\u20133676.","DOI":"10.24963\/ijcai.2019\/509"},{"issue":"12","key":"10.1016\/j.engappai.2026.115601_b31","doi-asserted-by":"crossref","first-page":"7891","DOI":"10.1109\/TITS.2021.3072743","article-title":"Metro passenger flow prediction via dynamic hypergraph convolution networks","volume":"22","author":"Wang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"5","key":"10.1016\/j.engappai.2026.115601_b32","doi-asserted-by":"crossref","first-page":"2576","DOI":"10.1109\/TCSS.2025.3534159","article-title":"Exploring human mobility correlations using semi-supervised hypergraph clustering","volume":"12","author":"Wang","year":"2025","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"4","key":"10.1016\/j.engappai.2026.115601_b33","doi-asserted-by":"crossref","first-page":"5496","DOI":"10.1109\/TCSS.2024.3372856","article-title":"Traffic origin-destination demand prediction via multichannel hypergraph convolutional networks","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"12","key":"10.1016\/j.engappai.2026.115601_b34","doi-asserted-by":"crossref","first-page":"19549","DOI":"10.1109\/TITS.2024.3461735","article-title":"Self-attention graph convolution imputation network for spatio-temporal traffic data","volume":"25","author":"Wei","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.engappai.2026.115601_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101939","article-title":"MEGACare: Knowledge-guided multi-view hypergraph predictive framework for healthcare","volume":"100","author":"Wu","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.115601_b36","series-title":"International Conference on Machine Learning","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","author":"Xie","year":"2016"},{"key":"10.1016\/j.engappai.2026.115601_b37","article-title":"Hypergcn: A new method for training graph convolutional networks on hypergraphs","volume":"32","author":"Yadati","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.115601_b38","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ins.2020.07.018","article-title":"Graphlshc: towards large scale spectral hypergraph clustering","volume":"544","author":"Yang","year":"2021","journal-title":"Inform. Sci."},{"key":"10.1016\/j.engappai.2026.115601_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2022.103564","article-title":"Identifying intracity freight trip ends from heavy truck GPS trajectories","volume":"136","author":"Yang","year":"2022","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"10.1016\/j.engappai.2026.115601_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.commtr.2022.100068","article-title":"Understanding travel behavior adjustment under COVID-19","volume":"2","author":"Yao","year":"2022","journal-title":"Commun. Transp. Res."},{"key":"10.1016\/j.engappai.2026.115601_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108586","article-title":"A novel reconstruction method for displacement missing data of arch dam via hierarchical clustering and deep learning","volume":"133","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.engappai.2026.115601_b42","first-page":"198","article-title":"Exploring temporal variability in travel patterns on public transit using big smart card data","volume":"50","author":"Zhao","year":"2023","journal-title":"Environ. Plan. B: Urban Anal. City Sci."},{"key":"10.1016\/j.engappai.2026.115601_b43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03081079.2024.2380917","article-title":"Unraveling the propagation dynamics of passenger congestion in public transportation systems using big smart card data","author":"Zhao","year":"2024","journal-title":"Int. J. Gen. Syst."},{"key":"10.1016\/j.engappai.2026.115601_b44","series-title":"2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), Gold Coast, Australia","first-page":"4074","article-title":"Adaptive clustering of trucks with high-order mobility correlations using enhanced hypergraph convolutional networks","author":"Zhao","year":"2025"},{"issue":"1","key":"10.1016\/j.engappai.2026.115601_b45","first-page":"163","article-title":"Modeling relation proximity of passengers using public transit smart card data","volume":"14","author":"Zhao","year":"2020","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"issue":"8","key":"10.1016\/j.engappai.2026.115601_b46","doi-asserted-by":"crossref","first-page":"4825","DOI":"10.1109\/TITS.2020.2983853","article-title":"Interactive visual exploration of human mobility correlation based on smart card data","volume":"22","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"3","key":"10.1016\/j.engappai.2026.115601_b47","first-page":"181","article-title":"Detecting pickpocketing gangs on buses with smart card data","volume":"11","author":"Zhao","year":"2019","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"10.1016\/j.engappai.2026.115601_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111536","article-title":"Contrastive deep graph clustering via higher-order heuristic augmentation and propagation","volume":"159","author":"Zheng","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115601_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.120012","article-title":"Adaptive multi-channel contrastive graph convolutional network with graph and feature fusion","volume":"658","author":"Zhong","year":"2024","journal-title":"Inform. Sci."},{"issue":"6","key":"10.1016\/j.engappai.2026.115601_b50","doi-asserted-by":"crossref","first-page":"2476","DOI":"10.1109\/TKDE.2023.3322129","article-title":"Focusedcleaner: Sanitizing poisoned graphs for robust gnn-based node classification","volume":"36","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"5","key":"10.1016\/j.engappai.2026.115601_b51","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-022-1349-5","article-title":"Representation learning via an integrated autoencoder for unsupervised domain adaptation","volume":"17","author":"Zhu","year":"2023","journal-title":"Front. Comput. Sci."},{"issue":"3","key":"10.1016\/j.engappai.2026.115601_b52","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1057\/s41278-021-00194-7","article-title":"Impacts of short-term measures to decarbonize maritime transport on perishable cargoes","volume":"24","author":"Zis","year":"2021","journal-title":"Marit. Econ. Logist."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626018853?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626018853?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T03:01:29Z","timestamp":1784084489000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626018853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":52,"alternative-id":["S0952197626018853"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115601","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Adaptive hypergraph clustering of trucks based on high-order mobility correlations","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115601","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115601"}}