{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T01:50:16Z","timestamp":1726105816277},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030638351"},{"type":"electronic","value":"9783030638368"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-63836-8_28","type":"book-chapter","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T09:12:07Z","timestamp":1605690727000},"page":"331-342","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Prediction of Taxi Demand Based on CNN-BiLSTM-Attention Neural Network"],"prefix":"10.1007","author":[{"given":"Xudong","family":"Guo","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Yang, Q., Gao, Z., Kong, X., Rahim, A., Wang, J., Xia, F.: Taxi operation optimization based on big traffic data. In: 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp. 127\u2013134. IEEE (2015)","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP.2015.42"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Kong, X., Xu, Z., Shen, G., Wang, J., Yang, Q., Zhang, B.: Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Gener. Comput. Syst. 61, 97\u2013107 (2016)","DOI":"10.1016\/j.future.2015.11.013"},{"issue":"1","key":"28_CR3","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1109\/TITS.2014.2328231","volume":"16","author":"D Zhang","year":"2015","unstructured":"Zhang, D., et al.: Understanding taxi service strategies from taxi GPS traces. IEEE Trans. Intell. Transp. Syst. 16(1), 123\u2013135 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Zhao, K., Khryashchev, D., Freire, J., Silva, C., Vo, H.: Predicting taxi demand at high spatial resolution: approaching the limit of predictability. In: IEEE International Conference on Big Data (2017)","DOI":"10.1109\/BigData.2016.7840676"},{"key":"28_CR5","unstructured":"Qian, X., Ukkusuri, S., Yang, C., Yan, F.: Short term taxi demand forecasting using Gaussian conditional random field model. In: Transportation Research Board 2017 Annual Meeting (2017)"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Yan, H., Zhang, Z., Zou, J.: An online spatio-temporal model for inference and predictions of taxi demand. In: IEEE International Conference on Big Data (2017)","DOI":"10.1109\/BigData.2017.8258345"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction (2016)","DOI":"10.1609\/aaai.v31i1.10735"},{"issue":"8","key":"28_CR8","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TITS.2017.2755684","volume":"19","author":"X Jun","year":"2018","unstructured":"Jun, X., Rahmatizadeh, R., B\u00f6l\u00f6ni, L., Turgut, D.: Real-time prediction of taxi demand using recurrent neural networks. IEEE Trans. Intell. Transp. Syst. 19(8), 2572\u20132581 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"8","key":"28_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"28_CR10","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. PP(99), 1\u201313 (2019)"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Kunihiko and Fukushima: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193\u2013202 (1980)","DOI":"10.1007\/BF00344251"},{"issue":"11","key":"28_CR12","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"28_CR13","doi-asserted-by":"publisher","unstructured":"Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-24797-2","DOI":"10.1007\/978-3-642-24797-2"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Zhou, P., Shi, W., Tian, J., Qi, Z., Xu, B.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (2016)","DOI":"10.18653\/v1\/P16-2034"},{"key":"28_CR15","unstructured":"NYC taxi & limousine commission. Taxi and limousine commission (TLC) trip record data. https:\/\/www1.nyc.gov\/site\/tlc\/about\/tlc-trip-record-data.page. Accessed 13 Sept 2020"},{"key":"28_CR16","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)"},{"key":"28_CR17","unstructured":"Wikipedia. https:\/\/en.wikipedia.org\/wiki\/Symmetric_mean_absolute_percentage_error. Accessed 13 Sept 2020"},{"key":"28_CR18","unstructured":"Vanguard software homepage. https:\/\/www.vanguardsw.com\/business-forecasting-101\/forecast-fit\/. Accessed 13 Sept 2020"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63836-8_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:32:22Z","timestamp":1710250342000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63836-8_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030638351","9783030638368"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63836-8_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangkok","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apnns.org\/ICONIP2020","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":"618","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":"187","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":"189","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":"3.18","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":"3.68","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)"}},{"value":"Due to COVID-19 pandemic the conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}