{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:28:14Z","timestamp":1763202494967,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757618"},{"type":"electronic","value":"9783030757625"}],"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\/978-3-030-75762-5_58","type":"book-chapter","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T09:07:43Z","timestamp":1620464863000},"page":"741-753","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1700-9215","authenticated-orcid":false,"given":"Hao","family":"Xue","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1237-1664","authenticated-orcid":false,"given":"Flora D.","family":"Salim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,9]]},"reference":[{"key":"58_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"58_CR2","doi-asserted-by":"crossref","unstructured":"Feng, J., et al.: Deepmove: predicting human mobility with attentional recurrent networks. In: WWW (2018)","DOI":"10.1145\/3178876.3186058"},{"key":"58_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"58_CR4","unstructured":"Jiang, R., et al.: VLUC: an empirical benchmark for video-like urban computing on citywide crowd and traffic prediction. arXiv preprint arXiv:1911.06982 (2019)"},{"issue":"1","key":"58_CR5","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3141\/1857-09","volume":"1857","author":"Y Kamarianakis","year":"2003","unstructured":"Kamarianakis, Y., Prastacos, P.: Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transp. Res. Rec. 1857(1), 74\u201384 (2003)","journal-title":"Transp. Res. Rec."},{"key":"58_CR6","doi-asserted-by":"publisher","first-page":"2052003","DOI":"10.1142\/S0218001420520035","volume":"34","author":"Y Kang","year":"2019","unstructured":"Kang, Y., Yang, B., Li, H., Chen, T., Zhang, Y.: Deep spatio-temporal modified-inception with dilated convolution networks for citywide crowd flows prediction. Int. J. Pattern Recognit Artif Intell. 34, 2052003 (2019)","journal-title":"Int. J. Pattern Recognit Artif Intell."},{"key":"58_CR7","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: SIGIR, pp. 95\u2013104 (2018)","DOI":"10.1145\/3209978.3210006"},{"issue":"1","key":"58_CR8","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s11704-011-1192-6","volume":"6","author":"X Li","year":"2012","unstructured":"Li, X., et al.: Prediction of urban human mobility using large-scale taxi traces and its applications. Front. Comp. Sci. 6(1), 111\u2013121 (2012). https:\/\/doi.org\/10.1007\/s11704-011-1192-6","journal-title":"Front. Comp. Sci."},{"key":"58_CR9","doi-asserted-by":"crossref","unstructured":"Lin, Z., Feng, J., Lu, Z., Li, Y., Jin, D.: DeepSTN+: context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In: AAAI, vol. 33, pp. 1020\u20131027 (2019)","DOI":"10.1609\/aaai.v33i01.33011020"},{"issue":"2","key":"58_CR10","first-page":"871","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 T-ITS 14(2), 871\u2013882 (2013)","journal-title":"IEEE T-ITS"},{"key":"58_CR11","unstructured":"Santoro, A., et al.: A simple neural network module for relational reasoning. In: NeurIPS, pp. 4967\u20134976 (2017)"},{"key":"58_CR12","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: NeurIPS (2019)"},{"issue":"1","key":"58_CR13","doi-asserted-by":"publisher","first-page":"116","DOI":"10.3141\/2024-14","volume":"2024","author":"S Shekhar","year":"2007","unstructured":"Shekhar, S., Williams, B.M.: Adaptive seasonal time series models for forecasting short-term traffic flow. Transp. Res. Rec. 2024(1), 116\u2013125 (2007)","journal-title":"Transp. Res. Rec."},{"key":"58_CR14","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS, pp. 5998\u20136008 (2017)"},{"issue":"6","key":"58_CR15","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","volume":"129","author":"BM Williams","year":"2003","unstructured":"Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664\u2013672 (2003)","journal-title":"J. Transp. Eng."},{"key":"58_CR16","doi-asserted-by":"crossref","unstructured":"Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"58_CR17","doi-asserted-by":"crossref","unstructured":"Yao, H., et al.: Deep multi-view spatial-temporal network for taxi demand prediction. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"58_CR18","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":"58_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X.: DNN-based prediction model for spatio-temporal data. In: ACM SIGSPATIAL, pp. 1\u20134 (2016)","DOI":"10.1145\/2996913.2997016"},{"key":"58_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X., Li, T.: Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif. Intell. 259, pp. 147\u2013166 (2018)","DOI":"10.1016\/j.artint.2018.03.002"},{"key":"58_CR21","doi-asserted-by":"crossref","unstructured":"Zonoozi, A., Kim, J.j., Li, X.L., Cong, G.: Periodic-CRN: a convolutional recurrent model for crowd density prediction with recurring periodic patterns. In: IJCAI, pp. 3732\u20133738 (2018)","DOI":"10.24963\/ijcai.2018\/519"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75762-5_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:23:46Z","timestamp":1710350626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75762-5_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757618","9783030757625"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75762-5_58","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":"9 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","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":"157","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":"0","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":"23% - 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","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)"}}]}}