{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:59:26Z","timestamp":1743040766575,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030926373"},{"type":"electronic","value":"9783030926380"}],"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\/978-3-030-92638-0_23","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T13:02:45Z","timestamp":1641042165000},"page":"385-400","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geographic and\u00a0Temporal Deep Learning Method for\u00a0Traffic Flow Prediction in\u00a0Highway Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7146-8829","authenticated-orcid":false,"given":"Tianpu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9982-5488","authenticated-orcid":false,"given":"Weilong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengda","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongkang","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.future.2019.08.026","volume":"102","author":"W Ding","year":"2020","unstructured":"Ding, W., Wang, X., Zhao, Z.: CO-STAR: a collaborative prediction service for short-term trends on continuous spatio-temporal data. Futur. Gener. Comput. Syst. 102, 481\u2013493 (2020)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery Data Mining (KDD), pp. 316\u2013324 (2011)","DOI":"10.1145\/2020408.2020462"},{"issue":"3","key":"23_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)","volume":"121","author":"MM Hamed","year":"1995","unstructured":"Hamed, M.M., Al-Masaeid, H.R., Said, Z.M.B.: Short-term prediction of traffic volume in urban arterials. J. Transp. Eng. 121(3), 249\u2013254 (1995)","journal-title":"J. Transp. Eng."},{"issue":"5","key":"23_CR4","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1111\/0885-9507.00200","volume":"15","author":"P Lingras","year":"2000","unstructured":"Lingras, P., Sharma, S.C., Osborne, P., Kalyar, I.: Traffic volume time-series analysis according to the type of road use. Comput.-Aided Civil Infrastruct. Eng. 15(5), 365\u2013373 (2000)","journal-title":"Comput.-Aided Civil Infrastruct. Eng."},{"key":"23_CR5","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-0-387-32348-0_11","volume-title":"Modeling Financial Time Series with S-Plus R","author":"E Zivot","year":"2006","unstructured":"Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. In: Zivot, E., Wang, J. (eds.) Modeling Financial Time Series with S-Plus R, pp. 385\u2013429. Springer, New York (2006). https:\/\/doi.org\/10.1007\/978-0-387-32348-0_11"},{"issue":"2","key":"23_CR6","first-page":"865","volume":"16","author":"Y Lv","year":"2015","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 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"23_CR7","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TITS.2004.837813","volume":"5","author":"C-H 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":"23_CR8","first-page":"178","volume":"24","author":"X Zhang","year":"2009","unstructured":"Zhang, X., He, G., Lu, H.: Short-term traffic flow forecasting based on K-nearest neighbors non-parametric regression. J. Syst. Eng. 24(2), 178\u2013183 (2009)","journal-title":"J. Syst. Eng."},{"issue":"1","key":"23_CR9","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/TITS.2006.869623","volume":"7","author":"S Sun","year":"2006","unstructured":"Sun, S., Zhang, C., Yu, G.: A Bayesian network approach to traffic flow forecasting. IEEE Trans. Intell. Transp. Syst. 7(1), 124\u2013132 (2006)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"23_CR10","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1111\/j.1467-9876.2012.01059.x","volume":"62","author":"O Anacleto","year":"2013","unstructured":"Anacleto, O., Queen, C., Albers, C.J.: Multivariate forecasting of road traffic flows in the presence of heteroscedasticity and measurement errors. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 62(2), 251\u2013270 (2013)","journal-title":"J. Roy. Stat. Soc. Ser. C (Appl. Stat.)"},{"issue":"7553","key":"23_CR11","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)","journal-title":"Nature"},{"key":"23_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"issue":"Jan","key":"23_CR13","first-page":"1","volume":"10","author":"H Larochelle","year":"2009","unstructured":"Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10(Jan), 1\u201340 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"23_CR14","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":"23_CR15","doi-asserted-by":"publisher","first-page":"4883","DOI":"10.1109\/TITS.2019.2950416","volume":"21","author":"Z Cui","year":"2019","unstructured":"Cui, Z., Henrickson, K., Ke, R., Wang, Y.: Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Trans. Intell. Transp. Syst. 21(11), 4883\u20134894 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"23_CR16","unstructured":"Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1596\u20131607 (2018)"},{"key":"23_CR17","unstructured":"Rangapuram, S.S., Seeger, M.W., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T.: Deep state space models for time series forecasting. In: Advances in Neural Information Processing Systems, pp. 7785\u20137794 (2018)"},{"key":"23_CR18","unstructured":"Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: bidirectional recurrent imputation for time series. In: Advances in Neural Information Processing Systems, pp. 6775\u20136785 (2018)"},{"key":"23_CR19","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: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 95\u2013104 (2018)","DOI":"10.1145\/3209978.3210006"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X.: DNN-based prediction model for spatio-temporal data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1\u20134 (2016)","DOI":"10.1145\/2996913.2997016"},{"key":"23_CR21","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":"23_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/978-3-030-16145-3_3","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"L Bai","year":"2019","unstructured":"Bai, L., Yao, L., Kanhere, S.S., Yang, Z., Chu, J., Wang, X.: Passenger demand forecasting with multi-task convolutional recurrent neural networks. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) PAKDD 2019. LNCS (LNAI), vol. 11440, pp. 29\u201342. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-16145-3_3"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Yao, H., et al.: Deep multi-view spatial-temporal network for taxi demand prediction. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"23_CR24","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844\u20133852 (2016)"},{"key":"23_CR25","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Chen, C., et al.: Gated residual recurrent graph neural networks for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 485\u2013492 (2019)","DOI":"10.1609\/aaai.v33i01.3301485"},{"key":"23_CR27","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural networks: data-driven traffic forecasting. In: Proceedings of the International Conference on Learning Representations (2018)"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal sychronous graph convolutional networks: a new framework for spatial-temporal network data forecasting (2020). https:\/\/github.com\/wanhuaiyu\/STSGCN\/blob\/master\/paper\/AAAI2020-STSGCN.pdf","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 1907\u20131913. AAAI Press (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, L., Xie, Y., Cao, W., Gao, Y., Feng, X.: Multirange attentive bicomponent graph convolutional network for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i04.5758"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Diao, Z., Wang, X., Zhang, D., Liu, Y., Xie, K., He, S.: Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 890\u2013897 (2019)","DOI":"10.1609\/aaai.v33i01.3301890"},{"key":"23_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13677-019-0149-4","volume":"9","author":"W Ding","year":"2020","unstructured":"Ding, W., et al.: An ensemble-learning method for potential traffic hotspots detection on heterogeneous spatio-temporal data in highway domain. J. Cloud Comput. Adv. Syst. Appl. 9, 1\u201311 (2020)","journal-title":"J. Cloud Comput. Adv. Syst. Appl."},{"key":"23_CR33","doi-asserted-by":"publisher","unstructured":"Ding, W., Zhao, Z.: DS-harmonizer: a harmonization service on spatiotemporal data stream in edge computing environment. Wirel. Commun. Mob. Comput. 2018, Article ID 9354273, 12 p (2018). https:\/\/doi.org\/10.1155\/2018\/9354273","DOI":"10.1155\/2018\/9354273"},{"issue":"4","key":"23_CR34","first-page":"290","volume":"7","author":"W Ding","year":"2020","unstructured":"Ding, W., Zou, J., Zhao, Z.: A multidimensional service template for data analysis in highway domain. Int. J. Internet Manuf. Serv. 7(4), 290 (2020)","journal-title":"Int. J. Internet Manuf. Serv."},{"issue":"1","key":"23_CR35","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1504\/IJIITC.2019.104735","volume":"1","author":"J Zhou","year":"2019","unstructured":"Zhou, J., Ding, W.: An evolutionary service solution for spatio-temporal data analysis in highway domain. Int. J. Intell. Internet Things Comput. 1(1), 43\u201352 (2019)","journal-title":"Int. J. Intell. Internet Things Comput."},{"key":"23_CR36","unstructured":"Kip, F.T.N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks (2016)"},{"key":"23_CR37","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-030-30146-0_7","volume-title":"Collaborative Computing: Networking, Applications and Worksharing","author":"J Zhou","year":"2019","unstructured":"Zhou, J., Ding, W., Zhao, Z., Li, H.: SMART: a service-oriented statistical analysis framework on spatio-temporal big data (short paper). In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds.) CollaborateCom 2019. LNICSSITE, vol. 292, pp. 91\u2013100. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30146-0_7"},{"key":"23_CR38","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-030-67540-0_15","volume-title":"Collaborative Computing: Networking, Applications and Worksharing","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Ding, W., Wang, H.: A hybrid deep learning approach for traffic flow prediction in highway domain. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds.) CollaborateCom 2020. LNICSSITE, vol. 350, pp. 253\u2013267. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67540-0_15"},{"key":"23_CR39","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/978-3-030-30146-0_46","volume-title":"Collaborative Computing: Networking, Applications and Worksharing","author":"W Ding","year":"2019","unstructured":"Ding, W., Wang, Z., Zhao, Z.: A platform service for passenger volume analysis on massive smart card data in public transportation domain. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds.) CollaborateCom 2019. LNICSSITE, vol. 292, pp. 681\u2013697. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30146-0_46"},{"key":"23_CR40","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1007\/978-3-030-00916-8_35","volume-title":"Collaborative Computing: Networking, Applications and Worksharing","author":"W Ding","year":"2018","unstructured":"Ding, W., Zhao, Z., Li, H., Cao, Y., Xu, Y.: A passenger flow analysis method through ride behaviors on massive smart card data. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds.) CollaborateCom 2017. LNICSSITE, vol. 252, pp. 374\u2013382. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00916-8_35"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92638-0_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T12:17:58Z","timestamp":1674303478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92638-0_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030926373","9783030926380"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92638-0_23","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","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":"16 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colcom2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/collaboratecom.eai-conferences.org\/2021\/","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":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"206","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":"62","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":"30% - 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":"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)"}}]}}