{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:16:19Z","timestamp":1778782579276,"version":"3.51.4"},"reference-count":66,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012401","name":"Beijing Science and Technology Planning Project","doi-asserted-by":"publisher","award":["Z211100004121013"],"award-info":[{"award-number":["Z211100004121013"]}],"id":[{"id":"10.13039\/501100012401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.eswa.2026.131486","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T07:46:21Z","timestamp":1770104781000},"page":"131486","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MagTCN: A multi-scale adaptive graph-enhanced temporal convolutional network for variance-imbalanced multivariate passenger flow forecasting"],"prefix":"10.1016","volume":"312","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6754-0981","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4093-0459","authenticated-orcid":false,"given":"Jianyuan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6519-8316","authenticated-orcid":false,"given":"Yong","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2161-4637","authenticated-orcid":false,"given":"Limin","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.131486_bib0001","series-title":"Proceedings of the 34th international conference on neural information processing systems","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume":"vol. 33","author":"Bai","year":"2020"},{"key":"10.1016\/j.eswa.2026.131486_bib0002","unstructured":"Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv: 1803.01271. 10.48550\/arXiv.1803.01271."},{"key":"10.1016\/j.eswa.2026.131486_bib0003","series-title":"Time series analysis: Forecasting and control","author":"Box","year":"2015"},{"key":"10.1016\/j.eswa.2026.131486_bib0004","series-title":"International conference on machine learning","first-page":"1204","article-title":"Graphnorm: A principled approach to accelerating graph neural network training","author":"Cai","year":"2021"},{"key":"10.1016\/j.eswa.2026.131486_bib0005","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"11141","article-title":"MSGNet: Learning multi-scale inter-series correlations for multivariate time series forecasting","volume":"vol. 38","author":"Cai","year":"2024"},{"issue":"10","key":"10.1016\/j.eswa.2026.131486_bib0006","doi-asserted-by":"crossref","first-page":"6913","DOI":"10.1109\/TNNLS.2022.3183903","article-title":"Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting","volume":"34","author":"Chen","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.131486_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111026","article-title":"SiGNN: A spike-induced graph neural network for dynamic graph representation learning","volume":"158","author":"Chen","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2026.131486_bib0008","series-title":"International conference on machine learning","first-page":"933","article-title":"Language modeling with gated convolutional networks","author":"Dauphin","year":"2017"},{"key":"10.1016\/j.eswa.2026.131486_bib0009","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131486_bib0010","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.neucom.2023.01.009","article-title":"\u03b4Free-LSTM: An error distribution free deep learning for short-term traffic flow forecasting","volume":"526","author":"Fang","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.131486_bib0011","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","volume":"vol. 33","author":"Guo","year":"2019"},{"key":"10.1016\/j.eswa.2026.131486_bib0012","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.trc.2019.08.005","article-title":"Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system","volume":"107","author":"Hao","year":"2019","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"10.1016\/j.eswa.2026.131486_bib0013","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"2","key":"10.1016\/j.eswa.2026.131486_bib0014","article-title":"Short-term forecasting of origin\u2013destination matrix in transit system via a deep learning approach","volume":"19","author":"He","year":"2023","journal-title":"Transportmetrica A: Transport Science"},{"key":"10.1016\/j.eswa.2026.131486_bib0015","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.eswa.2026.131486_bib0016","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"17359","article-title":"Adaptive multi-scale decomposition framework for time series forecasting","volume":"vol. 39","author":"Hu","year":"2025"},{"key":"10.1016\/j.eswa.2026.131486_bib0017","first-page":"46885","article-title":"CrossGnn: Confronting noisy multivariate time series via cross interaction refinement","volume":"36","author":"Huang","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131486_bib0018","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.trc.2014.03.016","article-title":"Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China","volume":"44","author":"Jiang","year":"2014","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"10.1016\/j.eswa.2026.131486_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126428","article-title":"Ada-STGMAT: An adaptive spatio-temporal graph multi-attention network for intelligent time series forecasting in smart cities","volume":"269","author":"Jin","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"7","key":"10.1016\/j.eswa.2026.131486_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-025-11223-9","article-title":"A comprehensive survey of deep learning for time series forecasting: Architectural diversity and open challenges","volume":"58","author":"Kim","year":"2025","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.eswa.2026.131486_bib0021","series-title":"International conference on learning representations","article-title":"Reversible instance normalization for accurate time-series forecasting against distribution shift","author":"Kim","year":"2021"},{"key":"10.1016\/j.eswa.2026.131486_bib0022","series-title":"International conference on learning representations","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.eswa.2026.131486_bib0023","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.tranpol.2018.06.005","article-title":"Modeling departure time choice of metro passengers with a smart corrected mixed logit model: A case study in beijing","volume":"69","author":"Li","year":"2018","journal-title":"Transport Policy"},{"issue":"4","key":"10.1016\/j.eswa.2026.131486_bib0024","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","article-title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting","volume":"37","author":"Lim","year":"2021","journal-title":"International Journal of Forecasting"},{"key":"10.1016\/j.eswa.2026.131486_bib0025","series-title":"International conference on learning representations","article-title":"Network in network","author":"Lin","year":"2014"},{"key":"10.1016\/j.eswa.2026.131486_bib0026","unstructured":"Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023). SegRNN: Segment recurrent neural network for long-term time series forecasting. arXiv: 2308.11200,. 10.48550\/arXiv.2308.11200."},{"key":"10.1016\/j.eswa.2026.131486_bib0027","series-title":"2020\u202fIEEE 4th information technology, networking, electronic and automation control conference (ITNEC)","first-page":"2520","article-title":"Short-term metro passenger flow prediction based on random forest and LSTM","volume":"vol. 1","author":"Lin","year":"2020"},{"issue":"10","key":"10.1016\/j.eswa.2026.131486_bib0028","doi-asserted-by":"crossref","first-page":"12472","DOI":"10.1007\/s10489-022-04122-x","article-title":"STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting","volume":"53","author":"Liu","year":"2022","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.eswa.2026.131486_bib0029","series-title":"The twelfth international conference on learning representations","article-title":"iTransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2024"},{"key":"10.1016\/j.eswa.2026.131486_bib0030","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.trc.2019.01.027","article-title":"DeepPF: A deep learning based architecture for metro passenger flow prediction","volume":"101","author":"Liu","year":"2019","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"10.1016\/j.eswa.2026.131486_bib0031","series-title":"Technical Report","article-title":"Passenger flows in underground railway stations and platforms","author":"Loukaitou-Sideris","year":"2015"},{"issue":"6","key":"10.1016\/j.eswa.2026.131486_bib0032","doi-asserted-by":"crossref","first-page":"5615","DOI":"10.1109\/TITS.2021.3055258","article-title":"Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method","volume":"23","author":"Ma","year":"2022","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"4","key":"10.1016\/j.eswa.2026.131486_bib0033","doi-asserted-by":"crossref","first-page":"3728","DOI":"10.1109\/TITS.2021.3117835","article-title":"A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction","volume":"24","author":"Ma","year":"2023","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"23","key":"10.1016\/j.eswa.2026.131486_bib0034","doi-asserted-by":"crossref","first-page":"38221","DOI":"10.1109\/JIOT.2024.3443910","article-title":"MPFormer: Multipatch transformer for multivariate time-series anomaly detection with contrastive learning","volume":"11","author":"Ma","year":"2024","journal-title":"IEEE Internet of Things Journal"},{"issue":"24","key":"10.1016\/j.eswa.2026.131486_bib0035","doi-asserted-by":"crossref","first-page":"17245","DOI":"10.1007\/s00521-021-06315-w","article-title":"Forecasting vehicular traffic flow using MLP and LSTM","volume":"33","author":"Oliveira","year":"2021","journal-title":"Neural Computing and Applications"},{"key":"10.1016\/j.eswa.2026.131486_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121325","article-title":"Sequence to sequence hybrid bi-LSTM model for traffic speed prediction","volume":"236","author":"Ounoughi","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131486_bib0037","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"5363","article-title":"EvolveGCN: Evolving graph convolutional networks for dynamic graphs","volume":"vol. 34","author":"Pareja","year":"2020"},{"key":"10.1016\/j.eswa.2026.131486_bib0038","unstructured":"Qiu, X., Cheng, H., Wu, X., Hu, J., Guo, C., & Yang, B. (2025). A comprehensive survey of deep learning for multivariate time series forecasting: A channel strategy perspective. arXiv: 2502.10721."},{"key":"10.1016\/j.eswa.2026.131486_bib0039","first-page":"2488","article-title":"How does batch normalization help optimization?","volume":"31","author":"Santurkar","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"10.1016\/j.eswa.2026.131486_bib0040","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3390\/systems13020095","article-title":"Crowd management at turnstiles in metro stations: A pilot study based on observation and microsimulation","volume":"13","author":"Seriani","year":"2025","journal-title":"Systems"},{"key":"10.1016\/j.eswa.2026.131486_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120259","article-title":"DAGCRN: Graph convolutional recurrent network for traffic forecasting with dynamic adjacency matrix","volume":"227","author":"Shi","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131486_bib0042","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neucom.2015.03.085","article-title":"A novel wavelet-SVM short-time passenger flow prediction in beijing subway system","volume":"166","author":"Sun","year":"2015","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.eswa.2026.131486_bib0043","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at scale","volume":"72","author":"Taylor","year":"2018","journal-title":"The American Statistician"},{"key":"10.1016\/j.eswa.2026.131486_bib0044","series-title":"The twelfth international conference on learning representations","article-title":"Freedyg: Frequency enhanced continuous-time dynamic graph model for link prediction","author":"Tian","year":"2024"},{"key":"10.1016\/j.eswa.2026.131486_bib0045","series-title":"Advances in neural information processing systems","first-page":"6000","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.eswa.2026.131486_bib0046","series-title":"International conference on learning representations","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018"},{"issue":"15","key":"10.1016\/j.eswa.2026.131486_bib0047","doi-asserted-by":"crossref","first-page":"5204","DOI":"10.1109\/JLT.2024.3393709","article-title":"Transformer-based spatio-temporal traffic prediction for access and metro networks","volume":"42","author":"Wang","year":"2024","journal-title":"Journal of Lightwave Technology"},{"key":"10.1016\/j.eswa.2026.131486_bib0048","series-title":"The eleventh international conference on learning representations","article-title":"Micn: Multi-scale local and global context modeling for long-term series forecasting","author":"Wang","year":"2023"},{"key":"10.1016\/j.eswa.2026.131486_bib0049","series-title":"The twelfth international conference on learning representations","article-title":"Timemixer: Decomposable multiscale mixing for time series forecasting","author":"Wang","year":"2024"},{"key":"10.1016\/j.eswa.2026.131486_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2022.127959","article-title":"Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network","volume":"604","author":"Wang","year":"2022","journal-title":"Physica A"},{"issue":"6","key":"10.1016\/j.eswa.2026.131486_bib0051","article-title":"Towards simulation optimization of subway station considering refined passenger behaviors","volume":"19","author":"Wang","year":"2024","journal-title":"PloS One"},{"key":"10.1016\/j.eswa.2026.131486_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111463","article-title":"A lightweight multi-layer perceptron for efficient multivariate time series forecasting","volume":"288","author":"Wang","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.131486_bib0053","series-title":"The eleventh international conference on learning representations","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2023"},{"key":"10.1016\/j.eswa.2026.131486_bib0054","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131486_bib0055","series-title":"Proceedings of the 28th international joint conference on artificial intelligence","article-title":"Graph wavenet for deep spatial-temporal graph modeling","author":"Wu","year":"2019"},{"key":"10.1016\/j.eswa.2026.131486_bib0056","first-page":"1","article-title":"Correlation-based feature selection and parallel spatio-temporal networks for efficient passenger flow forecasting in metro systems","author":"Xiu","year":"2024","journal-title":"Transportmetrica A: Transport Science"},{"issue":"18","key":"10.1016\/j.eswa.2026.131486_bib0057","doi-asserted-by":"crossref","DOI":"10.3390\/app12189114","article-title":"Short-time traffic forecasting in tourist service areas based on a CNN and GRU neural network","volume":"12","author":"Yang","year":"2022","journal-title":"Applied Sciences"},{"key":"10.1016\/j.eswa.2026.131486_bib0058","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.119144","article-title":"Short-term passenger flow prediction for multi-traffic modes: A transformer and residual network based multi-task learning method","volume":"642","author":"Yang","year":"2023","journal-title":"Information Sciences"},{"key":"10.1016\/j.eswa.2026.131486_bib0059","series-title":"Proceedings of the 27th international joint conference on artificial intelligence","first-page":"3634","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2018"},{"key":"10.1016\/j.eswa.2026.131486_bib0060","article-title":"Root mean square layer normalization","volume":"32","author":"Zhang","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.eswa.2026.131486_bib0061","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.32604\/cmc.2023.039274","article-title":"Kalman filter-based CNN-biLSTM-ATT model for traffic flow prediction","volume":"76","author":"Zhang","year":"2023","journal-title":"Computers, Materials & Continua"},{"issue":"9","key":"10.1016\/j.eswa.2026.131486_bib0062","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.eswa.2026.131486_bib0063","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"1234","article-title":"GMAN: A graph multi-attention network for traffic prediction","volume":"vol. 34","author":"Zheng","year":"2020"},{"key":"10.1016\/j.eswa.2026.131486_bib0064","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"11106","article-title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","volume":"vol. 35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.eswa.2026.131486_bib0065","series-title":"International conference on machine learning","first-page":"27268","article-title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","author":"Zhou","year":"2022"},{"issue":"2","key":"10.1016\/j.eswa.2026.131486_bib0066","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1108\/JICV-03-2021-0004","article-title":"Dynamic prediction of traffic incident duration on urban expressways: A deep learning approach based on LSTM and MLP","volume":"4","author":"Zhu","year":"2021","journal-title":"Journal of Intelligent and Connected Vehicles"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426003994?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426003994?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:49:33Z","timestamp":1778780973000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426003994"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":66,"alternative-id":["S0957417426003994"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131486","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MagTCN: A multi-scale adaptive graph-enhanced temporal convolutional network for variance-imbalanced multivariate passenger flow forecasting","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131486","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":"131486"}}