{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T12:14:39Z","timestamp":1762863279569,"version":"3.41.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02474-z","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T06:19:58Z","timestamp":1733120398000},"page":"3693-3710","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Quadratic memory-augmented spatio-temporal transformer graph convolutional recurrent network for traffic forecasting"],"prefix":"10.1007","volume":"16","author":[{"given":"Xiaoyan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yongqin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiangfu","family":"Meng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"2474_CR1","unstructured":"Snyder C, Do M (2019) Streets: a novel camera network dataset for traffic flow. In: Advances in neural information processing systems, vol 32"},{"key":"2474_CR2","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926"},{"key":"2474_CR3","doi-asserted-by":"crossref","unstructured":"Bai L, Yao L, Kanhere S, Wang X, Sheng et al (2019) STG2Seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting. arXiv preprint arXiv:1905.10069","DOI":"10.24963\/ijcai.2019\/274"},{"key":"2474_CR4","doi-asserted-by":"crossref","unstructured":"Jiang R, Yin D, Wang Z, Wang Y, Deng J, Liu H, Cai Z, Deng J, Song X, Shibasaki R (2021) DL-Traff: survey and benchmark of deep learning models for urban traffic prediction. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 4515\u20134525","DOI":"10.1145\/3459637.3482000"},{"key":"2474_CR5","doi-asserted-by":"crossref","unstructured":"Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 33. pp 922\u2013929","DOI":"10.1609\/aaai.v33i01.3301922"},{"issue":"6","key":"2474_CR6","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.1109\/TITS.2021.3054840","volume":"23","author":"X Yin","year":"2021","unstructured":"Yin X, Wu G, Wei J, Shen Y, Qi H, Yin B (2021) Deep learning on traffic prediction: methods, analysis, and future directions. IEEE Trans Intell Transp Syst 23(6):4927\u20134943","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2474_CR7","doi-asserted-by":"crossref","unstructured":"Xu R, Miao H, Wang S, Yu PS, Wang J (2024) PeFAD: a parameter-efficient federated framework for time series anomaly detection. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 3621\u20133632","DOI":"10.1145\/3637528.3671753"},{"key":"2474_CR8","doi-asserted-by":"crossref","unstructured":"Ye J, Sun L, Du B, Fu Y, Xiong H (2021) Coupled layer-wise graph convolution for transportation demand prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 35. pp 4617\u20134625","DOI":"10.1609\/aaai.v35i5.16591"},{"key":"2474_CR9","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","DOI":"10.24963\/ijcai.2018\/505"},{"key":"2474_CR10","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121","DOI":"10.24963\/ijcai.2019\/264"},{"key":"2474_CR11","doi-asserted-by":"crossref","unstructured":"Jiang J, Han C, Zhao WX, Wang J (2023) PDFormer: propagation delay-aware dynamic long-range transformer for traffic flow prediction. arXiv preprint arXiv:2301.07945","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"2474_CR12","doi-asserted-by":"crossref","unstructured":"Liu H, Dong Z, Jiang R, Deng J, Deng J, Chen Q, Song X (2023) Spatio-temporal adaptive embedding makes vanilla transformer SOTA for traffic forecasting. arXiv preprint arXiv:2308.10425","DOI":"10.1145\/3583780.3615160"},{"key":"2474_CR13","doi-asserted-by":"crossref","unstructured":"Liu X, Lyu X, Zhang X, Gao J, Chen J (2022) Memory augmented graph learning networks for multivariate time series forecasting. In: Proceedings of the 31st ACM international conference on information & knowledge management, pp 4254\u20134258","DOI":"10.1145\/3511808.3557638"},{"key":"2474_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106046","volume":"121","author":"D Peng","year":"2023","unstructured":"Peng D, Zhang Y (2023) MA-GCN: a memory augmented graph convolutional network for traffic prediction. Eng Appl Artif Intell 121:106046","journal-title":"Eng Appl Artif Intell"},{"key":"2474_CR15","doi-asserted-by":"crossref","unstructured":"Jiang R, Wang Z, Yong J, Jeph P, Chen Q, Kobayashi Y, Song X, Fukushima S, Suzumura T (2023) Spatio-temporal meta-graph learning for traffic forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 8078\u20138086","DOI":"10.1609\/aaai.v37i7.25976"},{"issue":"8","key":"2474_CR16","doi-asserted-by":"publisher","first-page":"3681","DOI":"10.1109\/TKDE.2020.3025580","volume":"34","author":"S Wang","year":"2020","unstructured":"Wang S, Cao J, Philip SY (2020) Deep learning for spatio-temporal data mining: a survey. IEEE Trans Knowl Data Eng 34(8):3681\u20133700","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2474_CR17","doi-asserted-by":"crossref","unstructured":"Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 31","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"2474_CR18","doi-asserted-by":"publisher","first-page":"5388","DOI":"10.1109\/TKDE.2023.3333824","volume":"36","author":"G Jin","year":"2023","unstructured":"Jin G, Liang Y, Fang Y, Shao Z, Huang J, Zhang J, Zheng Y (2023) Spatio-temporal graph neural networks for predictive learning in urban computing: a survey. IEEE Trans Knowl Data Eng 36:5388\u20135408","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2474_CR19","doi-asserted-by":"crossref","unstructured":"Song C, Lin Y, Guo S, Wan H (2020) Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI Conference on artificial intelligence, vol 34, pp 914\u2013921","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"2474_CR20","doi-asserted-by":"crossref","unstructured":"Zheng C, Fan X, Wang C, Qi J (2020) GMAN: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 1234\u20131241","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"2474_CR21","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai L, Yao L, Li C, Wang X, Wang C (2020) Adaptive graph convolutional recurrent network for traffic forecasting. Adv Neural Inf Proc Syst 33:17804\u201317815","journal-title":"Adv Neural Inf Proc Syst"},{"key":"2474_CR22","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C (2020) Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 753\u2013763","DOI":"10.1145\/3394486.3403118"},{"key":"2474_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109670","volume":"142","author":"W Weng","year":"2023","unstructured":"Weng W, Fan J, Wu H, Hu Y, Tian H, Zhu F, Wu J (2023) A decomposition dynamic graph convolutional recurrent network for traffic forecasting. Pattern Recognit 142:109670","journal-title":"Pattern Recognit"},{"key":"2474_CR24","doi-asserted-by":"crossref","unstructured":"Ma M, Hu J, Jensen CS, Teng F, Han P, Xu Z, Li T (2024) Learning time-aware graph structures for spatially correlated time series forecasting. In: 2024 IEEE 40th international conference on data engineering (ICDE), IEEE. pp 4435\u20134448","DOI":"10.1109\/ICDE60146.2024.00338"},{"key":"2474_CR25","doi-asserted-by":"crossref","unstructured":"Miao H, Zhao Y, Guo C, Yang B, Zheng K, Huang F, Xie J, Jensen CS (2024) A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data. arXiv preprint arXiv:2404.14999","DOI":"10.1109\/ICDE60146.2024.00085"},{"issue":"5","key":"2474_CR26","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1007\/s00778-024-00872-x","volume":"33","author":"X Wu","year":"2024","unstructured":"Wu X, Wu X, Yang B, Zhou L, Guo C, Qiu X, Hu J, Sheng Z, Jensen CS (2024) AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting. VLDB J 33(5):1743\u20131770","journal-title":"VLDB J"},{"key":"2474_CR27","unstructured":"Miao H, Wang S, Zhang M, Guo D, Sun F, Yang F (2024) Deep multi-view channel-wise spatio-temporal network for traffic flow prediction. arXiv preprint arXiv:2404.15034"},{"issue":"1","key":"2474_CR28","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s13042-022-01689-2","volume":"15","author":"J Xia","year":"2024","unstructured":"Xia J, Wang S, Wang X, Xia M, Xie K, Cao J (2024) Multi-view Bayesian spatio-temporal graph neural networks for reliable traffic flow prediction. Int J Mach Learn Cybern 15(1):65\u201378","journal-title":"Int J Mach Learn Cybern"},{"key":"2474_CR29","doi-asserted-by":"publisher","first-page":"2671","DOI":"10.1109\/TKDE.2023.3324501","volume":"36","author":"Y Fang","year":"2023","unstructured":"Fang Y, Qin Y, Luo H, Zhao F, Zheng K (2023) STWave+: a multi-scale efficient spectral graph attention network with long-term trends for disentangled traffic flow forecasting. IEEE Trans Knowl Data Eng 36:2671\u20132685","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2474_CR30","doi-asserted-by":"crossref","unstructured":"Shao Z, Zhang Z, Wang F, Wei W, Xu Y (2022) Spatial-temporal identity: a simple yet effective baseline for multivariate time series forecasting. In: Proceedings of the 31st ACM international conference on information & knowledge management, pp 4454\u20134458","DOI":"10.1145\/3511808.3557702"},{"key":"2474_CR31","doi-asserted-by":"crossref","unstructured":"Liu C, Yang S, Xu Q, Li Z, Long C, Li Z, Zhao R (2024) Spatial-temporal large language model for traffic prediction. arXiv preprint arXiv:2401.10134","DOI":"10.1109\/MDM61037.2024.00025"},{"key":"2474_CR32","doi-asserted-by":"crossref","unstructured":"Zou D, Wang S, Li X, Peng H, Wang Y, Liu C, Sheng K, Zhang B (2024) Multispans: a multi-range spatial-temporal transformer network for traffic forecast via structural entropy optimization. In: Proceedings of the 17th ACM international conference on web search and data mining, pp. 1032\u20131041","DOI":"10.1145\/3616855.3635820"},{"key":"2474_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Z, Zhao X, Miao H, Zhang C, Zhao H, Zhang J (2023) AutoSTL: automated spatio-temporal multi-task learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 4902\u20134910","DOI":"10.1609\/aaai.v37i4.25616"},{"issue":"7","key":"2474_CR34","first-page":"7164","volume":"35","author":"H Miao","year":"2022","unstructured":"Miao H, Shen J, Cao J, Xia J, Wang S (2022) MBA-STNet: Bayes-enhanced discriminative multi-task learning for flow prediction. IEEE Trans Knowl Data Eng 35(7):7164\u20137177","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2474_CR35","unstructured":"Graves A, Wayne G, Danihelka I (2014) Neural Turing machines. arXiv preprint arXiv:1410.5401"},{"key":"2474_CR36","unstructured":"Weston J, Chopra S, Bordes A (2014) Memory networks. arXiv preprint arXiv:1410.3916"},{"key":"2474_CR37","unstructured":"Sukhbaatar S, Weston J, Fergus R et al (2015) End-to-end memory networks. In: Advances in neural information processing systems, vol 28"},{"key":"2474_CR38","unstructured":"Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T (2016) Meta-learning with memory-augmented neural networks. In: International conference on machine learning, PMLR. pp 1842\u20131850"},{"key":"2474_CR39","doi-asserted-by":"crossref","unstructured":"Han T, Xie W, Zisserman A (2020) Memory-augmented dense predictive coding for video representation learning. In: European conference on computer vision, Springer. pp 312\u2013329","DOI":"10.1007\/978-3-030-58580-8_19"},{"key":"2474_CR40","unstructured":"Chang Y-Y, Sun F-Y, Wu Y-H, Lin S-D (2018) A memory-network based solution for multivariate time-series forecasting. arXiv preprint arXiv:1809.02105"},{"key":"2474_CR41","doi-asserted-by":"crossref","unstructured":"Gong D, Liu L, Le V, Saha B, Mansour MR, Venkatesh S, Hengel Avd (2019) Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1705\u20131714","DOI":"10.1109\/ICCV.2019.00179"},{"key":"2474_CR42","doi-asserted-by":"crossref","unstructured":"Park H, Noh J, Ham B (2020) Learning memory-guided normality for anomaly detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14372\u201314381","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"2474_CR43","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30"},{"key":"2474_CR44","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"2474_CR45","doi-asserted-by":"crossref","unstructured":"Geng X, Li Y, Wang L, Zhang L, Yang Q, Ye J, Liu Y (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 3656\u20133663","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"2474_CR46","unstructured":"Shang C, Chen J, Bi J (2021) Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861"},{"key":"2474_CR47","doi-asserted-by":"crossref","unstructured":"Chen W, Chen X, Zhang J, Huang K (2017) Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 403\u2013412","DOI":"10.1109\/CVPR.2017.145"},{"key":"2474_CR48","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101"},{"issue":"1","key":"2474_CR49","first-page":"136","volume":"10","author":"Z Lu","year":"2016","unstructured":"Lu Z, Zhou C, Wu J, Jiang H, Cui S (2016) Integrating granger causality and vector auto-regression for traffic prediction of large-scale WLANs. KSII Trans Internet Inf Syst 10(1):136\u2013151","journal-title":"KSII Trans Internet Inf Syst"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02474-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02474-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02474-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:30:59Z","timestamp":1749270659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02474-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"references-count":49,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2474"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02474-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing fnancial interests or personal relationships that could have appeared to infuence the work reported in this paper. The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}