{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:39:42Z","timestamp":1743061182376,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031368219"},{"type":"electronic","value":"9783031368226"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-36822-6_32","type":"book-chapter","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T03:25:54Z","timestamp":1689305154000},"page":"372-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adjacent-DBSCAN Enhanced Time-Varying Multi-graph Convolution Network for Traffic Flow Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8830-0678","authenticated-orcid":false,"given":"Yinxin","family":"Bao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4614-1023","authenticated-orcid":false,"given":"Quan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,15]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.trc.2020.02.016","volume":"114","author":"L Li","year":"2020","unstructured":"Li, L., Jiang, R., He, Z., Chen, X., Zhou, X.: Trajectory data-based traffic flow studies: a revisit. Transp. Res. Part C: Emerg. Technol. 114, 225\u2013240 (2020)","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physa.2019.121065","volume":"527","author":"Q Hou","year":"2019","unstructured":"Hou, Q., Leng, J., Ma, G., Liu, W., Cheng, Y.: An adaptive hybrid model for short-term urban traffic flow prediction. Physica A 527, 1\u201310 (2019)","journal-title":"Physica A"},{"key":"32_CR3","first-page":"1","volume":"2022","author":"YA Pan","year":"2022","unstructured":"Pan, Y.A., Guo, J., Chen, Y., Li, S., Li, W.: Incorporating traffic flow model into a deep learning method for traffic state estimation: a hybrid stepwise modeling framework. J. Adv. Transp. 2022, 1\u201317 (2022)","journal-title":"J. Adv. Transp."},{"key":"32_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2022.110135","volume":"260","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Lu, Z., Wang, J., Chen, L.: FCM-GCN-based upstream and downstream dependence model for air traffic flow networks. Knowl.-Based Syst. 260, 1\u201311 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"32_CR5","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.neunet.2021.10.021","volume":"145","author":"A Ali","year":"2022","unstructured":"Ali, A., Zhu, Y., Zakarya, M.: Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw. 145, 233\u2013247 (2022)","journal-title":"Neural Netw."},{"doi-asserted-by":"crossref","unstructured":"Guo, C., Chen, C.H., Hwang, F.J., Chang, C.C., Chang, C.C.: Fast spatiotemporal learning framework for traffic flow forecasting. IEEE Trans. Intell. Transp. Syst. 1\u201311 (2022)","key":"32_CR6","DOI":"10.1109\/TITS.2022.3224039"},{"issue":"6","key":"32_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app12062890","volume":"12","author":"SY Han","year":"2022","unstructured":"Han, S.Y., Zhao, Q., Sun, Q.W., Zhou, J., Chen, Y.H.: EnGS-DGR: traffic flow forecasting with in-definite forecasting interval by ensemble GCN, Seq2Seq, and dynamic graph reconfiguration. Appl. Sci. 12(6), 1\u201314 (2022)","journal-title":"Appl. Sci."},{"issue":"9","key":"32_CR8","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao, L., Song, Y., Zhang, C.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848\u20133858 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: ICLR 2018, Vancouver, Canada, pp.\u00a01\u201316 (2018)","key":"32_CR9"},{"doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning frame-work for traffic forecasting. In: IJCAI 2018, pp. 3634\u20133640. IJCAI, Stockholm, Sweden (2018)","key":"32_CR10","DOI":"10.24963\/ijcai.2018\/505"},{"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: AAAI 2019, vol. 2019, pp. 922\u2013929. AAAI, Hawaii (2019)","key":"32_CR11","DOI":"10.1609\/aaai.v33i01.3301922"},{"doi-asserted-by":"crossref","unstructured":"Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: AAAI 2020, vol. 2020, pp. 914\u2013921. AAAI, Palo Alto (2020)","key":"32_CR12","DOI":"10.1609\/aaai.v34i01.5438"},{"doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: AAAI 2021, vol. 35, pp. 4189\u20134196. AAAI (2021)","key":"32_CR13","DOI":"10.1609\/aaai.v35i5.16542"},{"doi-asserted-by":"crossref","unstructured":"Jin, G., Li, F., Zhang, J., Wang, M., Huang, J.: Automated dilated spatio-temporal synchronous graph modeling for traffic prediction. IEEE Trans. Intell. Transp. Syst., 1\u201311 (2022)","key":"32_CR14","DOI":"10.1109\/TITS.2022.3195232"}],"container-title":["Lecture Notes in Computer Science","Advances and Trends in Artificial Intelligence. Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36822-6_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T03:29:19Z","timestamp":1689305359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36822-6_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031368219","9783031368226"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36822-6_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IEA\/AIE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"36","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ieaaie2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ieaaie2023.com\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"129","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":"50","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":"20","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":"39% - 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)"}}]}}