{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:14:31Z","timestamp":1742969671042,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031402852"},{"type":"electronic","value":"9783031402869"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-40286-9_11","type":"book-chapter","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T23:02:27Z","timestamp":1691535747000},"page":"121-136","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sampling Spatial-Temporal Attention Network for\u00a0Traffic Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4378-6731","authenticated-orcid":false,"given":"Mao","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5259-2875","authenticated-orcid":false,"given":"Yi","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1989-8231","authenticated-orcid":false,"given":"Liangzhe","family":"Han","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0157-1716","authenticated-orcid":false,"given":"Leilei","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"issue":"4","key":"11_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev.: Comput. Stat. 2(4), 433\u2013459 (2010)","journal-title":"Wiley Interdiscip. Rev.: Comput. Stat."},{"issue":"2","key":"11_CR2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1093\/biomet\/60.2.255","volume":"60","author":"H Akaike","year":"1973","unstructured":"Akaike, H.: Maximum likelihood identification of gaussian autoregressive moving average models. Biometrika 60(2), 255\u2013265 (1973)","journal-title":"Biometrika"},{"key":"11_CR3","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. Adv. Neural. Inf. Process. Syst. 33, 17804\u201317815 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR4","first-page":"17766","volume":"33","author":"D Cao","year":"2020","unstructured":"Cao, D., et al.: Spectral temporal graph neural network for multivariate time-series forecasting. Adv. Neural. Inf. Process. Syst. 33, 17766\u201317778 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"11_CR5","doi-asserted-by":"publisher","first-page":"96","DOI":"10.3141\/1748-12","volume":"1748","author":"C Chen","year":"2001","unstructured":"Chen, C., Petty, K., Skabardonis, A., Varaiya, P., Jia, Z.: Freeway performance measurement system: mining loop detector data. Transp. Res. Rec. 1748(1), 96\u2013102 (2001)","journal-title":"Transp. Res. Rec."},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: 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 (2019)","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., Xiong, H.: Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 547\u2013555 (2021)","DOI":"10.1145\/3447548.3467275"},{"issue":"8","key":"11_CR9","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":"11_CR10","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"11_CR11","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)"},{"issue":"2","key":"11_CR12","first-page":"865","volume":"16","author":"Y Lv","year":"2014","unstructured":"Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865\u2013873 (2014)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"11_CR13","unstructured":"MacQueen, J.: Classification and analysis of multivariate observations. In: 5th Berkeley Symp. Math. Statist. Probability, pp. 281\u2013297. University of California Los Angeles LA USA (1967)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhang, Z., Wang, F., Wei, W., Xu, Y.: Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, pp. 4454\u20134458 (2022)","DOI":"10.1145\/3511808.3557702"},{"key":"11_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: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 914\u2013921 (2020)","DOI":"10.1609\/aaai.v34i01.5438"},{"issue":"2","key":"11_CR16","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/S0968-090X(03)00004-4","volume":"11","author":"A Stathopoulos","year":"2003","unstructured":"Stathopoulos, A., Karlaftis, M.G.: A multivariate state space approach for urban traffic flow modeling and prediction. Transp. Res. Part C: Emerg. Technol. 11(2), 121\u2013135 (2003)","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"11_CR17","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)","DOI":"10.24963\/ijcai.2018\/505"},{"issue":"9","key":"11_CR20","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2019","unstructured":"Zhao, L., et al.: T-gcn: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848\u20133858 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: A graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 1234\u20131241 (2020)","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"11_CR22","unstructured":"Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. Modeling financial time series with S-PLUS\u00ae, pp. 385\u2013429 (2006)"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40286-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T23:10:38Z","timestamp":1691536238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40286-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031402852","9783031402869"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40286-9_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ksem2023.conferences.academy\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"395","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"114","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}