{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T23:30:03Z","timestamp":1791502203954,"version":"4.3.4"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004541","name":"Ministry of Human Resource Development","doi-asserted-by":"publisher","award":["DST\/ICPS\/CPS Individual\/2018\/1091"],"award-info":[{"award-number":["DST\/ICPS\/CPS Individual\/2018\/1091"]}],"id":[{"id":"10.13039\/501100004541","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06522-5","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T12:02:38Z","timestamp":1632744158000},"page":"983-994","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Performance improvement for metro passenger flow forecast using spatio-temporal deep neural network"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5266-2159","authenticated-orcid":false,"given":"Jaison","family":"Mulerikkal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sajanraj","family":"Thandassery","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinith","family":"Rejathalal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepa Merlin Dixon","family":"Kunnamkody","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"6522_CR1","doi-asserted-by":"publisher","first-page":"106435","DOI":"10.1016\/j.cie.2020.106435","volume":"143","author":"H Abbasimehr","year":"2020","unstructured":"Abbasimehr H, Shabani M, Yousefi M (2020) An optimized model using lstm network for demand forecasting. Comput Ind Eng 143:106435. https:\/\/doi.org\/10.1016\/j.cie.2020.106435","journal-title":"Comput Ind Eng"},{"key":"6522_CR2","doi-asserted-by":"crossref","unstructured":"Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1\u20136. IEEE","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"6522_CR3","doi-asserted-by":"crossref","unstructured":"Chi Z, Jiang Z, Kamruzzaman M, Hafshejani BA, Safarpour M (2021) Adaptive momentum-based optimization to train deep neural network for simulating the static stability of the composite structure. Engineering with Computers pp. 1\u201323","DOI":"10.1007\/s00366-021-01335-5"},{"key":"6522_CR4","unstructured":"Das S, Politis DN (2020) Predictive inference for locally stationary time series with an application to climate data. J Am Stat Assoc pp. 1\u201316"},{"key":"6522_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranpol.2019.05.003","author":"Z Gan","year":"2019","unstructured":"Gan Z, Feng T, Wu Y, Yang M, Timmermans H (2019) Station-based average travel distance and its relationship with urban form and land use: an analysis of smart card data in Nanjing City. China Transp Pol. https:\/\/doi.org\/10.1016\/j.tranpol.2019.05.003","journal-title":"China Transp Pol"},{"issue":"8","key":"6522_CR6","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 (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"6522_CR7","unstructured":"Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTexts. https:\/\/otexts.com\/fpp2\/stationarity.html"},{"key":"6522_CR8","doi-asserted-by":"publisher","first-page":"26573","DOI":"10.1109\/ACCESS.2020.2971341","volume":"8","author":"X Kong","year":"2020","unstructured":"Kong X, Gao H, Alfarraj O, Ni Q, Zheng C, Shen G (2020) Huad: hierarchical urban anomaly detection based on spatio-temporal data. IEEE Access 8:26573\u201326582","journal-title":"IEEE Access"},{"key":"6522_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s12469-013-0077-5","author":"SG Lee","year":"2014","unstructured":"Lee SG, Hickman M (2014) Trip purpose inference using automated fare collection data. Pub Transp. https:\/\/doi.org\/10.1007\/s12469-013-0077-5","journal-title":"Pub Transp"},{"key":"6522_CR10","first-page":"1776","volume":"2019","author":"J Lei","year":"2019","unstructured":"Lei J, He M, Shuai C (2019) A comparison study of short-term passenger flow forecast model of rail transit. CICTP 2019:1776\u20131787","journal-title":"CICTP"},{"key":"6522_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2867042","author":"X Ma","year":"2019","unstructured":"Ma X, Zhang J, Du B, Ding C, Sun L (2019) Parallel architecture of convolutional bi-directional LSTM neural networks for network-wide metro ridership prediction. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2018.2867042","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"3","key":"6522_CR12","doi-asserted-by":"publisher","first-page":"1891","DOI":"10.3150\/19-BEJ1174","volume":"26","author":"U Mayer","year":"2020","unstructured":"Mayer U, Z\u00e4hle H, Zhou Z et al (2020) Functional weak limit theorem for a local empirical process of non-stationary time series and its application. Bernoulli 26(3):1891\u20131911","journal-title":"Bernoulli"},{"key":"6522_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3091542","author":"J Mulerikkal","year":"2021","unstructured":"Mulerikkal J, Thandassery S, Rejathalal V, Ayyappan B et al (2021) Jp-dap: an intelligent data analytics platform for metro rail transport systems. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2021.3091542","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6522_CR14","unstructured":"Olah C Understanding lstm networks. http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/"},{"key":"6522_CR15","doi-asserted-by":"publisher","unstructured":"Park JY, Kim DJ, Lim Y (2008) Use of smart card data to define public transit use in Seoul. South Korea Transportation Research Record. https:\/\/doi.org\/10.3141\/2063-01","DOI":"10.3141\/2063-01"},{"key":"6522_CR16","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"6522_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.121075","author":"Y Qu","year":"2019","unstructured":"Qu Y, Xiao Y, Liu H, Yin H, Wu J, Qu Q, Li D, Tang T (2019) Analyzing crowd dynamic characteristics of boarding and alighting process in urban metro stations. Phys A Stat Mech Appl. https:\/\/doi.org\/10.1016\/j.physa.2019.121075","journal-title":"Phys A Stat Mech Appl"},{"key":"6522_CR18","doi-asserted-by":"crossref","unstructured":"Satriawan YS, Saputro JS (2020) An application of support vector machine to detect anomalies in time series data. In: AIP Conference Proceedings, vol. 2217, p. 030201. AIP Publishing LLC","DOI":"10.1063\/5.0000659"},{"key":"6522_CR19","unstructured":"Sch\u00f6lkopf B, Williamson RC, Smola AJ, Shawe-Taylor J, Platt JC, et\u00a0al. (1999) Support vector method for novelty detection. In: NIPS, vol.\u00a012, pp. 582\u2013588. Citeseer"},{"key":"6522_CR20","doi-asserted-by":"publisher","first-page":"106523","DOI":"10.1016\/j.knosys.2020.106523","volume":"211","author":"Z Shi","year":"2021","unstructured":"Shi Z, Bai Y, Jin X, Wang X, Su T, Kong J (2021) Parallel deep prediction with covariance intersection fusion on non-stationary time series. Knowl Based Syst 211:106523. https:\/\/doi.org\/10.1016\/j.knosys.2020.106523","journal-title":"Knowl Based Syst"},{"key":"6522_CR21","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.jairtraman.2019.04.005","volume":"78","author":"S Sun","year":"2019","unstructured":"Sun S, Lu H, Tsui KL, Wang S (2019) Nonlinear vector auto-regression neural network for forecasting air passenger flow. J Air Transp Manag 78:54\u201362","journal-title":"J Air Transp Manag"},{"issue":"1","key":"6522_CR22","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s10182-006-0220-6","volume":"90","author":"J Wolters","year":"2006","unstructured":"Wolters J, Hassler U (2006) Unit root testing. Allgemeines Stati Archiv 90(1):43\u201358","journal-title":"Allgemeines Stati Archiv"},{"issue":"1","key":"6522_CR23","doi-asserted-by":"publisher","first-page":"141","DOI":"10.3390\/su13010141","volume":"13","author":"Q Yu","year":"2021","unstructured":"Yu Q, Li W, Yang D, Xie Y (2021) Policy zoning for efficient land utilization based on spatio-temporal integration between the bicycle-sharing service and the metro transit. Sustainability 13(1):141","journal-title":"Sustainability"},{"key":"6522_CR24","doi-asserted-by":"publisher","first-page":"28475","DOI":"10.1109\/ACCESS.2020.2971771","volume":"8","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Wang C, Gao Y, Chen Y, Chen J (2020) Passenger flow forecast of rail station based on multi-source data and long short term memory network. IEEE Access 8:28475\u201328483","journal-title":"IEEE Access"},{"key":"6522_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2679179","author":"J Zhao","year":"2017","unstructured":"Zhao J, Qu Q, Zhang F, Xu C, Liu S (2017) Spatio-temporal analysis of passenger travel patterns in massive smart card data. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2017.2679179","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6522_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2587864","author":"J Zhao","year":"2017","unstructured":"Zhao J, Zhang F, Tu L, Xu C, Shen D, Tian C, Li XY, Li Z (2017) Estimation of passenger route choice pattern using smart card data for complex metro systems. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2016.2587864","journal-title":"IEEE Trans Intell Transp Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06522-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06522-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06522-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T15:39:34Z","timestamp":1642779574000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06522-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":26,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6522"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06522-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,27]]},"assertion":[{"value":"27 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}