{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:29:06Z","timestamp":1742912946432,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863647"},{"type":"electronic","value":"9783030863654"}],"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\/978-3-030-86365-4_21","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:02:39Z","timestamp":1631271759000},"page":"255-266","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["STGATP: A Spatio-Temporal Graph Attention Network for Long-Term Traffic Prediction"],"prefix":"10.1007","author":[{"given":"Mengting","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Xianqiang","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"issue":"3","key":"21_CR1","doi-asserted-by":"publisher","first-page":"1840","DOI":"10.1109\/TITS.2020.3025687","volume":"22","author":"C Chen","year":"2021","unstructured":"Chen, C., Liu, B., Wan, S., Qiao, P., Pei, Q.: An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 22(3), 1840\u20131852 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"21_CR2","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.neucom.2020.07.009","volume":"413","author":"L Chen","year":"2020","unstructured":"Chen, L., Zheng, L., Yang, J., Xia, D., Liu, W.: Short-term traffic flow prediction: from the perspective of traffic flow decomposition. Neurocomputing 413, 444\u2013456 (2020)","journal-title":"Neurocomputing"},{"issue":"12","key":"21_CR3","doi-asserted-by":"publisher","first-page":"12301","DOI":"10.1109\/TVT.2019.2947080","volume":"68","author":"L Han","year":"2019","unstructured":"Han, L., Zheng, K., Zhao, L., Wang, X., Shen, X.: Short-term traffic prediction based on deep cluster in large-scale road networks. IEEE Trans. Veh. Technol. 68(12), 12301\u201312313 (2019)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"21_CR4","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":"21_CR5","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.neucom.2018.12.016","volume":"332","author":"B Yang","year":"2019","unstructured":"Yang, B., Sun, S., Li, J., Lin, X., Tian, Y.: Traffic flow prediction using LSTM with feature enhancement. Neurocomputing 332, 320\u2013327 (2019)","journal-title":"Neurocomputing"},{"issue":"2","key":"21_CR6","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1080\/23249935.2019.1637966","volume":"15","author":"W Zhang","year":"2019","unstructured":"Zhang, W., Yu, Y., Qi, Y., Shu, F., Wang, Y.: Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transportmetrica A Transp. Sci. 15(2), 1688\u20131711 (2019)","journal-title":"Transportmetrica A Transp. Sci."},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Boukerche, A., Wang, J.: Machine learning-based traffic prediction models for intelligent transportation systems. Comput. Netw. 181, 107530 (2020)","DOI":"10.1016\/j.comnet.2020.107530"},{"issue":"4","key":"21_CR8","doi-asserted-by":"publisher","first-page":"818","DOI":"10.3390\/s17040818","volume":"17","author":"X Ma","year":"2017","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818 (2017)","journal-title":"Sensors"},{"issue":"1","key":"21_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"21_CR10","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (2018)"},{"key":"21_CR11","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020)","journal-title":"AI Open"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. In: The 28th International Joint Conference on Artificial Intelligence (IJCAI). International Joint Conferences on Artificial Intelligence Organization (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"21_CR13","unstructured":"Guo, K., et al.: Optimized graph convolution recurrent neural network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21, 3848\u20133858 (2020)"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"He, Z., Chow, C.Y., Zhang, J.D.: STCNN: a spatio-temporal convolutional neural network for long-term traffic prediction. In: 2019 20th IEEE International Conference on Mobile Data Management (MDM), pp. 226\u2013233. IEEE (2019)","DOI":"10.1109\/MDM.2019.00-53"},{"key":"21_CR15","doi-asserted-by":"publisher","first-page":"81606","DOI":"10.1109\/ACCESS.2020.2991462","volume":"8","author":"N Ranjan","year":"2020","unstructured":"Ranjan, N., Bhandari, S., Zhao, H.P., Kim, H., Khan, P.: City-wide traffic congestion prediction based on CNN, LSTM and transpose CNN. IEEE Access 8, 81606\u201381620 (2020)","journal-title":"IEEE Access"},{"key":"21_CR16","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":"21_CR17","unstructured":"van den Oord, A., et al.: WaveNet: a generative model for raw audio. In: The 9th ISCA Speech Synthesis Workshop, Sunnyvale, CA, USA, 13\u201315 September 2016. p. 125. ISCA (2016)"},{"key":"21_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-3-030-01249-6_34","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Mehta","year":"2018","unstructured":"Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., Hajishirzi, H.: ESPNet: efficient spatial pyramid of dilated convolutions for semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 561\u2013580. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_34"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.: Revisiting dilated convolution: a simple approach for weakly-and semi-supervised semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7268\u20137277 (2018)","DOI":"10.1109\/CVPR.2018.00759"},{"key":"21_CR20","first-page":"131","volume":"16","author":"J Treur","year":"2016","unstructured":"Treur, J.: Dynamic modeling based on a temporal-causal network modeling approach. Biol. Insp. Cogn. Architect. 16, 131\u2013168 (2016)","journal-title":"Biol. Insp. Cogn. Architect."},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Pandey, A., Wang, D.: TCNN: temporal convolutional neural network for real-time speech enhancement in the time domain. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6875\u20136879. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683634"},{"key":"21_CR22","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)"},{"issue":"4","key":"21_CR23","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","volume":"34","author":"MM Bronstein","year":"2017","unstructured":"Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34(4), 18\u201342 (2017)","journal-title":"IEEE Signal Process. Mag."},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Shanthamallu, U.S., Thiagarajan, J.J., Spanias, A.: A regularized attention mechanism for graph attention networks. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3372\u20133376. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9054363"},{"issue":"3","key":"21_CR25","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1002\/(SICI)1099-131X(199705)16:3<147::AID-FOR652>3.0.CO;2-X","volume":"16","author":"S Makridakis","year":"1997","unstructured":"Makridakis, S., Hibon, M.: ARMA models and the box-Jenkins methodology. J. Forecast. 16(3), 147\u2013163 (1997)","journal-title":"J. Forecast."},{"issue":"4","key":"21_CR26","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TITS.2004.837813","volume":"5","author":"CH Wu","year":"2004","unstructured":"Wu, C.H., Ho, J.M., Lee, D.T.: Travel-time prediction with support vector regression. IEEE Trans. Intell. Transp. Syst. 5(4), 276\u2013281 (2004)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"21_CR27","doi-asserted-by":"publisher","first-page":"219","DOI":"10.5194\/isprsarchives-XL-2-W3-219-2014","volume":"40","author":"M Raeesi","year":"2014","unstructured":"Raeesi, M., Mesgari, M., Mahmoudi, P.: Traffic time series forecasting by feedforward neural network: a case study based on traffic data of Monroe. Int. Archiv. Photogram. Remote Sens. Spat. Inf. Sci. 40(2), 219 (2014)","journal-title":"Int. Archiv. Photogram. Remote Sens. Spat. Inf. Sci."},{"key":"21_CR28","first-page":"3104","volume":"27","author":"I Sutskever","year":"2014","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural. Inf. Process. Syst. 27, 3104\u20133112 (2014)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR29","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"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86365-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:07:07Z","timestamp":1631272027000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86365-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863647","9783030863654"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86365-4_21","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":"7 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","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":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","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":"265","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":"4","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":"53% - 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":"2.5","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)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}