{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:07:37Z","timestamp":1766138857276,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031703430"},{"type":"electronic","value":"9783031703447"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70344-7_5","type":"book-chapter","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T08:02:43Z","timestamp":1724918563000},"page":"72-88","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multivariate Traffic Demand Prediction via\u00a02D Spectral Learning and\u00a0Global Spatial Optimization"],"prefix":"10.1007","author":[{"given":"Changlu","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanbin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Bai, L., Yao, L., Kanhere, S., Wang, X., Sheng, Q., et\u00a0al.: Stg2seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting. arXiv preprint arXiv:1905.10069 (2019)","DOI":"10.24963\/ijcai.2019\/274"},{"key":"5_CR2","unstructured":"Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"5_CR3","unstructured":"Basri, R., Galun, M., Geifman, A., Jacobs, D., Kasten, Y., Kritchman, S.: Frequency bias in neural networks for input of non-uniform density. In: International Conference on Machine Learning, pp. 685\u2013694. PMLR (2020)"},{"issue":"3","key":"5_CR4","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1111\/tgis.12644","volume":"24","author":"L Cai","year":"2020","unstructured":"Cai, L., Janowicz, K., Mai, G., Yan, B., Zhu, R.: Traffic transformer: capturing the continuity and periodicity of time series for traffic forecasting. Trans. GIS 24(3), 736\u2013755 (2020)","journal-title":"Trans. GIS"},{"key":"5_CR5","unstructured":"Chi, L., Jiang, B., Mu, Y.: Fast Fourier convolution. In: Advances in Neural Information Processing Systems, vol. 33, pp. 4479\u20134488 (2020)"},{"key":"5_CR6","unstructured":"Distances, C.M.S.: Lightspeed computation of optimal transport. In: Advances in Neural Information Processing Systems, vol. 26, pp. 2292\u20132300 (2013)"},{"issue":"8","key":"5_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"5_CR8","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)"},{"key":"5_CR9","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105 (2012)"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Lee-Thorp, J., Ainslie, J., Eckstein, I., Ontanon, S.: Fnet: mixing tokens with Fourier transforms. arXiv preprint arXiv:2105.03824 (2021)","DOI":"10.18653\/v1\/2022.naacl-main.319"},{"key":"5_CR11","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International Conference on Learning Representations (2018)"},{"key":"5_CR12","unstructured":"Rahaman, N., et al.: On the spectral bias of neural networks. In: International Conference on Machine Learning, pp. 5301\u20135310. PMLR (2019)"},{"key":"5_CR13","unstructured":"Rahimi, A., Recht, B.: Random features for large-scale kernel machines. In: Advances in Neural Information Processing Systems, vol. 20 (2007)"},{"key":"5_CR14","unstructured":"Rao, Y., Zhao, W., Zhu, Z., Lu, J., Zhou, J.: Global filter networks for image classification. In: Advances in Neural Information Processing Systems, vol. 34, pp. 980\u2013993 (2021)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: AAAI, vol.\u00a034, pp. 914\u2013921 (2020)","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"5_CR16","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"5_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71050-9","volume-title":"Optimal Transport: Old and New","author":"C Villani","year":"2009","unstructured":"Villani, C., et al.: Optimal Transport: Old and New, vol. 338. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-540-71050-9"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Wang, B., Lin, Y., Guo, S., Wan, H.: Gsnet: learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i5.16566"},{"key":"5_CR19","unstructured":"Woo, G., Liu, C., Sahoo, D., Kumar, A., Hoi, S.: Cost: contrastive learning of disentangled seasonal-trend representations for time series forecasting. arXiv preprint arXiv:2202.01575 (2022)"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. In: IJCAI, pp. 1907\u20131913 (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"5_CR22","unstructured":"Xu, M., et al.: Spatial-temporal transformer networks for traffic flow forecasting. arXiv preprint arXiv:2001.02908 (2020)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Ye, J., et al.: Learning the evolutionary and multi-scale graph structure for multivariate time series forecasting. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2296\u20132306 (2022)","DOI":"10.1145\/3534678.3539274"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Ye, J., Sun, L., Du, B., Fu, Y., Tong, X., Xiong, H.: Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 305\u2013313 (2019)","DOI":"10.1145\/3292500.3330887"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Ye, J., Sun, L., Du, B., Fu, Y., Xiong, H.: Coupled layer-wise graph convolution for transportation demand prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 4617\u20134625 (2021)","DOI":"10.1609\/aaai.v35i5.16591"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 3634\u20133640 (2018)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"5_CR28","unstructured":"Zhang, X., Zhao, Z., Tsiligkaridis, T., Zitnik, M.: Self-supervised contrastive pre-training for time series via time-frequency consistency. arXiv preprint arXiv:2206.08496 (2022)"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: a graph multi-attention network for traffic prediction. In: AAAI, vol.\u00a034, pp. 1234\u20131241 (2020)","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"5_CR30","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: Fedformer: frequency enhanced decomposed transformer for long-term series forecasting. arXiv preprint arXiv:2201.12740 (2022)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70344-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T08:04:53Z","timestamp":1724918693000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70344-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703430","9783031703447"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70344-7_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This paper investigates the traffic demand forecasting problem. The used datasets are all publicly available and do not contain any personal information. Besides, we adhere to academic standards and ethical requirements to avoid any behavior that may violate these requirements.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}