{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:24:24Z","timestamp":1775816664681,"version":"3.50.1"},"reference-count":39,"publisher":"Informa UK Limited","issue":"6","content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent Transportation Systems"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1080\/15472450.2023.2279633","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T01:49:18Z","timestamp":1699840158000},"page":"1032-1043","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":16,"title":["How spatial features affect urban rail transit prediction accuracy: a deep learning based passenger flow prediction method"],"prefix":"10.1080","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1377-7914","authenticated-orcid":false,"given":"Shuang","family":"Li","sequence":"first","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing, China"},{"name":"Jiangsu Key Laboratory of Urban ITS, Nanjing, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern, Urban Traffic Technologies, Nanjing, China"}]},{"given":"Xiaoxi","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing, China"},{"name":"Jiangsu Key Laboratory of Urban ITS, Nanjing, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern, Urban Traffic Technologies, Nanjing, China"}]},{"given":"Meina","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Modern Post, Nanjing University of Posts and Telecommunications, Nanjing, China"}]},{"given":"Junlan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing, China"},{"name":"Jiangsu Key Laboratory of Urban ITS, Nanjing, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern, Urban Traffic Technologies, Nanjing, China"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing, China"}]},{"given":"Xiucheng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing, China"},{"name":"Jiangsu Key Laboratory of Urban ITS, Nanjing, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern, Urban Traffic Technologies, Nanjing, China"}]}],"member":"301","published-online":{"date-parts":[[2023,11,12]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.3141\/2526-15"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-9209(97)00009-6"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.landusepol.2012.04.017"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2021.3049362"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2021.09.010"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2019.05.028"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.multra.2022.100004"},{"key":"e_1_3_3_9_1","doi-asserted-by":"crossref","unstructured":"Cho K. van Merrienboer B. Bahdanau D. & Bengio Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. ArXiv:1409.1259 [Cs Stat]. http:\/\/arxiv.org\/abs\/1409.1259","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/NCM.2009.224"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2020.102332"},{"key":"e_1_3_3_12_1","volume-title":"Deep learning","author":"Goodfellow L.","year":"2016","unstructured":"Goodfellow, L., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. http:\/\/www.deeplearningbook.org"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8060243"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/9717582"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/itsc.2017.8317872"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2016.12.004"},{"key":"e_1_3_3_18_1","volume-title":"Adam: A method for stochastic optimization","author":"Kingma D. P.","year":"2014","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. Learning."},{"key":"e_1_3_3_19_1","unstructured":"Kipf T. N. & Welling M. (2017). Semi-supervised classification with graph convolutional networks. ArXiv:1609.02907 [Cs Stat]. http:\/\/arxiv.org\/abs\/1609.02907"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2022.2142049"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.01.027"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2867042"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.multra.2022.100052"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2017.09.016"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trpro.2017.07.008"},{"key":"e_1_3_3_26_1","first-page":"802","volume-title":"Advances in Neural Information Processing Systems","author":"Shi X.","year":"2015","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W., & Woo, W. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in Neural Information Processing Systems, pp. 802\u2013810."},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.20858\/sjsutst.2021.111.12"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.03.085"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2010.09.004"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8392592"},{"key":"e_1_3_3_31_1","unstructured":"Taylor B. D. Fink C. N. Y. (2003). The factors influencing transit ridership: A review and analysis of the ridership literature. University of California Transportation Center Working Papers. https:\/\/escholarship.org\/uc\/item\/3xk9j8m2"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.11.001"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207049"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219922"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2019.0873"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2941987"},{"key":"e_1_3_3_37_1","unstructured":"Zhang J. Zheng Y. & Qi D. (2017). Deep spatio-temporal residual networks for citywide crowd flows prediction. ArXiv:1610.00081 [Cs]. http:\/\/arxiv.org\/abs\/1610.00081"},{"key":"e_1_3_3_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2013.07.002"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2023.2209913"},{"key":"e_1_3_3_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"}],"container-title":["Journal of Intelligent Transportation Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/15472450.2023.2279633","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T12:59:18Z","timestamp":1730206758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/15472450.2023.2279633"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,12]]},"references-count":39,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10.1080\/15472450.2023.2279633"],"URL":"https:\/\/doi.org\/10.1080\/15472450.2023.2279633","relation":{},"ISSN":["1547-2450","1547-2442"],"issn-type":[{"value":"1547-2450","type":"print"},{"value":"1547-2442","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,12]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=gits20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=gits20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2021-10-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-29","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}