{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:12:09Z","timestamp":1743023529428,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030854614"},{"type":"electronic","value":"9783030854621"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-85462-1_7","type":"book-chapter","created":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T06:02:59Z","timestamp":1628920979000},"page":"83-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ST-GWANN: A Novel Spatial-Temporal Graph Wavelet Attention Neural Network for Traffic Prediction"],"prefix":"10.1007","author":[{"given":"Zunhao","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bowen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lutong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nannan","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,15]]},"reference":[{"issue":"6","key":"7_CR1","first-page":"73","volume":"10","author":"Y Jian","year":"2012","unstructured":"Jian, Y., Bingquan, F.: Synthesis of short-term traffic flow forecasting research progress. Urban Transpor. China 10(6), 73\u201379 (2012)","journal-title":"Urban Transpor. China"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/6575947","volume":"2017","author":"Y Jia","year":"2017","unstructured":"Jia, Y., Wu, J., Xu, M.: Traffic flow prediction with rainfall impact using a deep learning method. J. Adv. Transpor. 2017, 1\u201310 (2017)","journal-title":"J. Adv. Transpor."},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: International Joint Conference on Artificial Intelligence, pp. 3634\u20133640 (2017)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Wang, Z., Su, X., Ding, Z.: Long-term traffic prediction based on LSTM encoder-decoder architecture. IEEE Transac. Intellig. Transport. Syst. (2020)","DOI":"10.1109\/TITS.2020.2995546"},{"key":"7_CR5","unstructured":"Wu, Y., Tan, H.: Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. Comp. Sci. (2016)"},{"key":"7_CR6","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven Traffic Forecasting. (2017)"},{"key":"7_CR7","unstructured":"Xu, B., Shen, H., Cao, Q., Qiu, Y., Cheng, X.: Graph wavelet neural network. In: International conference for learning representations (ICLR 2019), pp.1\u201313. New Orleans, LA, USA (2019)"},{"key":"7_CR8","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)"},{"issue":"3","key":"7_CR9","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(3), 199\u2013222 (2004)","journal-title":"Stat. Comput."},{"issue":"3","key":"7_CR10","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). https:\/\/doi.org\/10.1007\/s12544-015-0170-8","journal-title":"Eur. Transp. Res. Rev."},{"issue":"2","key":"7_CR11","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."},{"issue":"8","key":"7_CR12","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":"7_CR13","doi-asserted-by":"crossref","unstructured":"Cho, K. et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. Comp. Sci. (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"7_CR14","unstructured":"Parmar, N. et al.: Image transformer. In: International Conference on Machine Learning, pp. 4055\u20134064, PMLR (2018)"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1 (2017)","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"7_CR16","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. In: Proceedings of International Conference on Learning Representations. (2014)"},{"key":"7_CR17","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: International Conference on Neural Information Processing, pp. 362\u2013373. Springer, Cham (2018)","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. In: International Joint Conference on Artificial Intelligence (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"01","key":"7_CR20","first-page":"922","volume":"33","author":"S Guo","year":"2019","unstructured":"Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc. AAAI Conf. Artif. Intellig. 33(01), 922\u2013929 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intellig."},{"key":"7_CR21","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering (2016)"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. In: IEEE Transactions on Neural Networks and Learning Systems (2020)","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"7_CR23","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2017)"},{"key":"7_CR24","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Comput. Sci. (2014)"},{"key":"7_CR25","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: International Conference on Machine Learning, pp. 933\u2013941. PMLR (2017)"},{"key":"7_CR26","unstructured":"Amap Open Platform. https:\/\/lbs.amap.com\/"}],"container-title":["Lecture Notes in Computer Science","Spatial Data and Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-85462-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T10:52:31Z","timestamp":1673088751000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85462-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030854614","9783030854621"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85462-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"15 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SpatialDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Spatial Data and Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"spatialdi2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dbl.zju.edu.cn\/~yjgao\/spatialdi\/en\/index.html","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":"72","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":"14","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":"7","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":"6","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)"}}]}}