{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:04:53Z","timestamp":1784012693857,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234431","type":"print"},{"value":"9789819234448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3444-8_5","type":"book-chapter","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T06:18:27Z","timestamp":1784009907000},"page":"49-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EDG-ODE: Edge-Driven High-Order Graph Neural ODE for Continuous Traffic Flow Forecasting"],"prefix":"10.1007","author":[{"given":"Jinhui","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zesheng","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117921","volume":"207","author":"W Jiang","year":"2022","unstructured":"Jiang, W., Luo, J.: Graph neural network for traffic forecasting: a survey. Expert Syst. Appl. 207, 117921 (2022)","journal-title":"Expert Syst. Appl."},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","volume":"43","author":"EI Vlahogianni","year":"2014","unstructured":"Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we\u2019re going. Transp. Res. C. 43, 3\u201319 (2014)","journal-title":"Transp. Res. C"},{"key":"5_CR3","volume-title":"ICLR","author":"Y Li","year":"2018","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: ICLR (2018)"},{"key":"5_CR4","volume-title":"IJCAI","author":"B Yu","year":"2018","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: IJCAI (2018)"},{"key":"5_CR5","volume-title":"IJCAI","author":"Z Wu","year":"2019","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X.: Graph WaveNet for deep spatial-temporal graph modeling. In: IJCAI (2019)"},{"key":"5_CR6","volume-title":"AAAI","author":"C Zheng","year":"2020","unstructured":"Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: a graph multi-attention network for traffic prediction. In: AAAI (2020)"},{"key":"5_CR7","volume-title":"NeurIPS","author":"RTQ Chen","year":"2018","unstructured":"Chen, R.T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.: Neural ordinary differential equations. In: NeurIPS (2018)"},{"key":"5_CR8","volume-title":"KDD","author":"Z Fang","year":"2021","unstructured":"Fang, Z., et al.: Spatial-temporal graph ode networks for traffic flow forecasting. In: KDD (2021)"},{"key":"5_CR9","volume-title":"NeurIPS","author":"Y Rubanova","year":"2019","unstructured":"Rubanova, Y., Chen, R.T.Q., Duvenaud, D.: Latent ordinary differential equations for irregularly-sampled time series. In: NeurIPS (2019)"},{"key":"5_CR10","volume-title":"NeurIPS","author":"P Kidger","year":"2020","unstructured":"Kidger, P., Morrill, J., Foster, J., Lyons, T.: Neural controlled differential equations for irregular time series. In: NeurIPS (2020)"},{"key":"5_CR11","unstructured":"Zhuang, J., Dvornek, N., Li, X., Tatikonda, S., Papademetris, X., Duncan, J.: Ordinary differential equations on graph networks. ICLR. (2020)"},{"key":"5_CR12","volume-title":"NeurIPS","author":"L Bai","year":"2020","unstructured":"Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. In: NeurIPS (2020)"},{"key":"5_CR13","unstructured":"Wu, J., Chen, L.: Continuously evolving graph neural controlled differential equations for traffic forecasting. arXiv preprint arXiv:2401.14695. (2024)"},{"key":"5_CR14","unstructured":"Fang, L., et al.: Self-interpretable graph neural network for continuous-time dynamic graphs. arXiv preprint arXiv:2405.19062. (2024)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Gao, M., et al.: Multi-layer graph neural ODEs for traffic forecasting. Neural Netw. (2026)","DOI":"10.1016\/j.neunet.2026.108540"},{"key":"5_CR16","volume-title":"ICML Workshop on Graph Representation Learning","author":"M Poli","year":"2019","unstructured":"Poli, M., Massaroli, S., Park, J., Yamashita, A., Asama, H.: Graph neural ordinary differential equations. In: ICML Workshop on Graph Representation Learning (2019)"},{"key":"5_CR17","volume-title":"NeurIPS","author":"M Defferrard","year":"2016","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NeurIPS (2016)"},{"key":"5_CR18","volume-title":"AAAI","author":"X Li","year":"2021","unstructured":"Li, X., et al.: Dynamic graph convolutional recurrent network for traffic prediction. In: AAAI (2021)"},{"key":"5_CR19","volume-title":"AAAI","author":"Z Shao","year":"2022","unstructured":"Shao, Z., et al.: Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. In: AAAI (2022)"},{"key":"5_CR20","volume-title":"AAAI","author":"C Jiang","year":"2023","unstructured":"Jiang, C., et al.: PDFormer: propagation delay-aware dynamic long-range transformer for traffic forecasting. In: AAAI (2023)"},{"key":"5_CR21","volume-title":"NeurIPS","author":"M Xu","year":"2023","unstructured":"Xu, M., et al.: PASTN: pre-trained spatial-temporal network for traffic forecasting. In: NeurIPS (2023)"},{"key":"5_CR22","volume-title":"NeurIPS","author":"S Massaroli","year":"2020","unstructured":"Massaroli, S., Poli, M., Park, J., Yamashita, A., Asama, H.: Dissecting neural ODEs. In: NeurIPS (2020)"},{"key":"5_CR23","volume-title":"ICLR","author":"L Ruthotto","year":"2019","unstructured":"Ruthotto, L., Haber, E.: Deep neural networks motivated by partial differential equations. In: ICLR (2019)"},{"issue":"01","key":"5_CR24","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. Intell. 33(01), 922\u2013929 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"5_CR25","first-page":"2286","volume-title":"IJCAI","author":"S Fang","year":"2019","unstructured":"Fang, S., Zhang, Q., Meng, G., Xiang, S., Pan, C.: GSTNet: global spatial-temporal network for traffic flow prediction. In: IJCAI, pp. 2286\u20132293 (2019)"},{"key":"5_CR26","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":"5_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-27752-1","volume-title":"New Introduction to Multiple Time Series Analysis","author":"H L\u00fctkepohl","year":"2005","unstructured":"L\u00fctkepohl, H.: New Introduction to Multiple Time Series Analysis. Springer (2005)"},{"issue":"3","key":"5_CR28","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."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3444-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T06:18:31Z","timestamp":1784009911000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3444-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"ISBN":["9789819234431","9789819234448"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3444-8_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]},"assertion":[{"value":"15 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}