{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:12:03Z","timestamp":1742911923277,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819970186"},{"type":"electronic","value":"9789819970193"}],"license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-7019-3_37","type":"book-chapter","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:02:57Z","timestamp":1699574577000},"page":"401-412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Zoom-Based AutoEncoder for\u00a0Origin-Destination Demand Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9842-1242","authenticated-orcid":false,"given":"Xiaojian","family":"Ma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1989-8231","authenticated-orcid":false,"given":"Liangzhe","family":"Han","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7128-9940","authenticated-orcid":false,"given":"Gang","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9601-610X","authenticated-orcid":false,"given":"Xu","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8948-3103","authenticated-orcid":false,"given":"Tongyu","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"unstructured":"Ballard, D.H.: Modular learning in neural networks. In: AAAI, vol. 647, pp. 279\u2013284 (1987)","key":"37_CR1"},{"doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","key":"37_CR2","DOI":"10.1145\/2939672.2939785"},{"issue":"8","key":"37_CR3","doi-asserted-by":"publisher","first-page":"3219","DOI":"10.1109\/TITS.2019.2924971","volume":"21","author":"KF Chu","year":"2019","unstructured":"Chu, K.F., Lam, A.Y., Li, V.O.: Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions. IEEE Trans. Intell. Transp. Syst. 21(8), 3219\u20133232 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"37_CR4","doi-asserted-by":"publisher","first-page":"127816","DOI":"10.1109\/ACCESS.2019.2939902","volume":"7","author":"Z Duan","year":"2019","unstructured":"Duan, Z., et al.: Prediction of city-scale dynamic taxi origin-destination flows using a hybrid deep neural network combined with travel time. IEEE Access 7, 127816\u2013127832 (2019)","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Han, L., et al.: Continuous-time and multi-level graph representation learning for origin-destination demand prediction. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 516\u2013524 (2022)","key":"37_CR5","DOI":"10.1145\/3534678.3539273"},{"doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","key":"37_CR6","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"37_CR7","doi-asserted-by":"publisher","first-page":"119835","DOI":"10.1016\/j.eswa.2023.119835","volume":"222","author":"B Huang","year":"2023","unstructured":"Huang, B., Ruan, K., Yu, W., Xiao, J., Xie, R., Huang, J.: ODformer: spatial-temporal transformers for long sequence origin-destination matrix forecasting against cross application scenario. Exp. Syst. Appl. 222, 119835 (2023)","journal-title":"Exp. Syst. Appl."},{"unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)","key":"37_CR8"},{"issue":"10","key":"37_CR9","doi-asserted-by":"publisher","first-page":"3875","DOI":"10.1109\/TITS.2019.2915525","volume":"20","author":"L Liu","year":"2019","unstructured":"Liu, L., Qiu, Z., Li, G., Wang, Q., Ouyang, W., Lin, L.: Contextualized spatial-temporal network for taxi origin-destination demand prediction. IEEE Trans. Intell. Transp. Syst. 20(10), 3875\u20133887 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"37_CR10","doi-asserted-by":"publisher","first-page":"5106","DOI":"10.1109\/TITS.2020.3047047","volume":"23","author":"P Noursalehi","year":"2021","unstructured":"Noursalehi, P., Koutsopoulos, H.N., Zhao, J.: Dynamic origin-destination prediction in urban rail systems: a multi-resolution spatio-temporal deep learning approach. IEEE Trans. Intell. Transp. Syst. 23(6), 5106\u20135115 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Shi, H., et al.: Predicting origin-destination flow via multi-perspective graph convolutional network. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1818\u20131821. IEEE (2020)","key":"37_CR11","DOI":"10.1109\/ICDE48307.2020.00178"},{"unstructured":"Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)","key":"37_CR12"},{"doi-asserted-by":"crossref","unstructured":"Wang, Y., Yin, H., Chen, H., Wo, T., Xu, J., Zheng, K.: Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1227\u20131235 (2019)","key":"37_CR13","DOI":"10.1145\/3292500.3330877"},{"doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)","key":"37_CR14","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"8","key":"37_CR15","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1177\/0361198120919399","volume":"2674","author":"X Xiong","year":"2020","unstructured":"Xiong, X., Ozbay, K., Jin, L., Feng, C.: Dynamic origin-destination matrix prediction with line graph neural networks and kalman filter. Transp. Res. Rec. 2674(8), 491\u2013503 (2020)","journal-title":"Transp. Res. Rec."},{"key":"37_CR16","doi-asserted-by":"publisher","first-page":"102851","DOI":"10.1016\/j.trc.2020.102851","volume":"122","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Xiao, F., Shen, M., Zhong, S.: DNEAT: a novel dynamic node-edge attention network for origin-destination demand prediction. Transp. Res. Part C Emerg. Technol. 122, 102851 (2021)","journal-title":"Transp. Res. Part C Emerg. Technol."}],"container-title":["Lecture Notes in Computer Science","PRICAI 2023: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7019-3_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:18:56Z","timestamp":1699575536000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7019-3_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"ISBN":["9789819970186","9789819970193"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7019-3_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"10 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"15 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2023\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"422","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":"95","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":"36","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":"3.4","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":"3.1","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)"}}]}}