{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T02:15:32Z","timestamp":1773195332999,"version":"3.50.1"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030161446","type":"print"},{"value":"9783030161453","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-16145-3_3","type":"book-chapter","created":{"date-parts":[[2019,4,4]],"date-time":"2019-04-04T02:50:37Z","timestamp":1554346237000},"page":"29-42","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks"],"prefix":"10.1007","author":[{"given":"Lei","family":"Bai","sequence":"first","affiliation":[]},{"given":"Lina","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Salil S.","family":"Kanhere","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Chu","sequence":"additional","affiliation":[]},{"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,22]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.trc.2017.10.016","volume":"85","author":"J Ke","year":"2017","unstructured":"Ke, J., et al.: Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transp. Res. Part C: Emerg. Technol. 85, 591\u2013608 (2017)","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Yao, H., et al.: Deep multi-view spatial-temporal network for taxi demand prediction. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Deng, D., et al.: Latent space model for road networks to predict time-varying traffic. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)","DOI":"10.1145\/2939672.2939860"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Wang, D., et al.: DeepSD: supply-demand prediction for online car-hailing services using deep neural networks. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE (2017)","DOI":"10.1109\/ICDE.2017.83"},{"issue":"3","key":"3_CR6","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1109\/TITS.2013.2262376","volume":"14","author":"L Moreira-Matias","year":"2013","unstructured":"Moreira-Matias, L., et al.: Predicting taxi-passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 14(3), 1393\u20131402 (2013)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"3_CR7","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11704-011-1192-6","volume":"6","author":"X Li","year":"2012","unstructured":"Li, X., et al.: Prediction of urban human mobility using large-scale taxi traces and its applications. Front. Comput. Sci. 6(1), 111\u2013121 (2012)","journal-title":"Front. Comput. Sci."},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3141\/2634-10","volume":"2634","author":"Y Li","year":"2017","unstructured":"Li, Y., et al.: Taxi booking mobile app order demand prediction based on short-term traffic forecasting. Transp. Res. Rec.: J. Transp. Res. Board 2634, 57\u201368 (2017)","journal-title":"Transp. Res. Rec.: J. Transp. Res. Board"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Yu, R., et al.: Deep learning: a generic approach for extreme condition traffic forecasting. In: Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2017)","DOI":"10.1137\/1.9781611974973.87"},{"issue":"3","key":"3_CR10","first-page":"38","volume":"5","author":"Y Zheng","year":"2014","unstructured":"Zheng, Y., et al.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. (TIST) 5(3), 38 (2014)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Chu, J., et al.: Passenger demand prediction with cellular footprints. In: 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE (2018)","DOI":"10.1109\/SAHCN.2018.8397114"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: DNN-based prediction model for spatio-temporal data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM (2016)","DOI":"10.1145\/2996913.2997016"},{"key":"3_CR13","unstructured":"Xingjian, S.H.I., et al.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems (2015)"},{"key":"3_CR14","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. ACM (2016)","DOI":"10.1145\/2939672.2939785"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-16145-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:00:54Z","timestamp":1710334854000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-16145-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030161446","9783030161453"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-16145-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macau","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.pakdd2019.org","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 Conf. Man. Toolkit CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"542","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":"137","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":"0","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":"25% - 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":"3.79","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":"5.85","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)"}},{"value":"In addition, there were 31 PAKDD 2019 Workshops' papers accepted for publication","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)"}}]}}