{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T12:23:58Z","timestamp":1753359838158,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031306365"},{"type":"electronic","value":"9783031306372"}],"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\/978-3-031-30637-2_27","type":"book-chapter","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T10:08:13Z","timestamp":1681380493000},"page":"414-430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ISTNet: Inception Spatial Temporal Transformer for\u00a0Traffic Prediction"],"prefix":"10.1007","author":[{"given":"Chu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jia","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Ran","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Zhongyu","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Bai, L., Yao, L., Kanhere, S., Wang, X., Sheng, Q., et al.: 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":"27_CR2","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)"},{"issue":"2","key":"27_CR3","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/72.279188","volume":"5","author":"JT Connor","year":"1994","unstructured":"Connor, J.T., Martin, R.D., Atlas, L.E.: Recurrent neural networks and robust time series prediction. IEEE Trans. Neural Netw. 5(2), 240\u2013254 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"key":"27_CR4","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"},{"issue":"11","key":"27_CR5","doi-asserted-by":"publisher","first-page":"5415","DOI":"10.1109\/TKDE.2021.3056502","volume":"34","author":"S Guo","year":"2021","unstructured":"Guo, S., Lin, Y., Wan, H., Li, X., Cong, G.: Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans. Knowl. Data Eng. 34(11), 5415\u20135428 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"27_CR6","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"issue":"3","key":"27_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12544-015-0170-8","volume":"7","author":"SV Kumar","year":"2015","unstructured":"Kumar, S.V., Vanajakshi, L.: Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur. Transp. Res. Rev. 7(3), 1\u20139 (2015)","journal-title":"Eur. Transp. Res. Rev."},{"key":"27_CR8","unstructured":"Lan, S., Ma, Y., Huang, W., Wang, W., Yang, H., Li, P.: DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: International Conference on Machine Learning, pp. 11906\u201311917. PMLR (2022)"},{"key":"27_CR9","first-page":"1","volume":"17","author":"F Li","year":"2021","unstructured":"Li, F., et al.: Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans. Knowl. Discov. Data (TKDD) 17, 1\u201321 (2021)","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4189\u20134196 (2021)","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"27_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":"27_CR12","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TITS.2013.2247040","volume":"14","author":"M Lippi","year":"2013","unstructured":"Lippi, M., Bertini, M., Frasconi, P.: Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans. Intell. Transp. Syst. 14(2), 871\u2013882 (2013)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"27_CR13","unstructured":"Liu, S., et al.: Pyraformer: low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International Conference on Learning Representations (2021)"},{"key":"27_CR14","unstructured":"Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? In: Advances in Neural Information Processing Systems, vol. 34, pp. 12116\u201312128 (2021)"},{"key":"27_CR15","unstructured":"Sen, R., Yu, H.F., Dhillon, I.S.: Think globally, act locally: a deep neural network approach to high-dimensional time series forecasting. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"27_CR16","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"27_CR17","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola, A.J., Sch\u00f6lkopf, B.: A tutorial on support vector regression. Stat. Comput. 14, 199\u2013222 (2004)","journal-title":"Stat. Comput."},{"key":"27_CR18","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"},{"key":"27_CR19","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the Web Conference 2020, pp. 1082\u20131092 (2020)","DOI":"10.1145\/3366423.3380186"},{"key":"27_CR21","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 & Data Mining, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"27_CR22","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":"27_CR23","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"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"27_CR25","unstructured":"Zheng, C., Fan, X., Pan, S., Wu, Z., Wang, C., Yu, P.S.: Spatio-temporal joint graph convolutional networks for traffic forecasting. arXiv preprint arXiv:2111.13684 (2021)"},{"key":"27_CR26","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"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30637-2_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:14:13Z","timestamp":1710263653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30637-2_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306365","9783031306372"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30637-2_27","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":"14 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","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":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"652","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":"125","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":"66","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":"19% - 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":"3","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":"7.3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}