{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T02:06:43Z","timestamp":1775182003492,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T00:00:00Z","timestamp":1542412800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-106-2218-E-324-002"],"award-info":[{"award-number":["MOST-106-2218-E-324-002"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-107-2221-E-324-018-MY2"],"award-info":[{"award-number":["MOST-107-2221-E-324-018-MY2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["and No. 61672442"],"award-info":[{"award-number":["and No. 61672442"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Planning Project of Fujian Province, China","award":["No. 2016Y0079"],"award-info":[{"award-number":["No. 2016Y0079"]}]},{"name":"Young Teacher Education and Research Development Project of Fujian Province","award":["No. JAT170416"],"award-info":[{"award-number":["No. JAT170416"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1007\/s12652-018-1135-2","type":"journal-article","created":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T01:23:10Z","timestamp":1542417790000},"page":"4515-4532","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Applying a multistage of input feature combination to random forest for improving MRT passenger flow prediction"],"prefix":"10.1007","volume":"10","author":[{"given":"Lijuan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7621-1988","authenticated-orcid":false,"given":"Rung-Ching","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiangfu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunzhi","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,17]]},"reference":[{"key":"1135_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"issue":"2","key":"1135_CR2","first-page":"135","volume":"38","author":"C Cai","year":"2014","unstructured":"Cai C, Yao E, Wang M, Zhang Y (2014) Prediction of urban railway station\u2019s entrance and exit passenger flow based on multiply ARIMA model. J Beijing Jiaotong Univ 38(2):135\u2013140","journal-title":"J Beijing Jiaotong Univ"},{"issue":"4","key":"1135_CR3","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1016\/j.jtrangeo.2010.04.003","volume":"19","author":"MC Chen","year":"2011","unstructured":"Chen MC, Wei Y (2011) Exploring time variants for short-term passenger flow. J Trans Geogr 19(4):488\u2013498","journal-title":"J Trans Geogr"},{"key":"1135_CR4","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.trc.2011.12.006","volume":"22","author":"C Chen","year":"2012","unstructured":"Chen C, Wang Y, Li L, Hu J, Zhang Z (2012) The retrieval of intra-day trend and its influence on traffic prediction. Transp Res Part C Emerg Technol 22:103\u2013118","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1135_CR5","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.asoc.2014.10.022","volume":"26","author":"R Chen","year":"2015","unstructured":"Chen R, Liang CY, Hong WC, Gu DX (2015) Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl Soft Comput 26:435\u2013443","journal-title":"Appl Soft Comput"},{"issue":"7","key":"1135_CR6","first-page":"210","volume":"33","author":"G Chen","year":"2016","unstructured":"Chen G, Duan MZ, Zhang L (2016a) Urban rail transit network traffic dynamic optimization estimation. Comput Simulat 33(7):210\u2013212, 233","journal-title":"Comput Simulat"},{"issue":"1","key":"1135_CR7","first-page":"15","volume":"6","author":"YL Chen","year":"2016","unstructured":"Chen YL, Sha YW, Zhu XL, Zhang XH (2016b) Prediction of shanghai metro line 16 passenger flow based on time series analysis-with Lingang avenue station as a study case. Oper Res Fuzz 6(1):15\u201326","journal-title":"Oper Res Fuzz"},{"key":"1135_CR8","unstructured":"Feng SW, Li Q (2013) Car ownership control in Chinese mega cities: Shanghai, Beijing and Guangzhou [Online]. \n                    https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3106623\n                    \n                  . Accessed 1 Sep 2013"},{"key":"1135_CR9","series-title":"Springer series in statistics","volume-title":"The elements of statistical learning: data mining, inference and prediction","author":"J Friedman","year":"2008","unstructured":"Friedman J, Hastie T, Tibshirani R (2008) The elements of statistical learning: data mining, inference and prediction. Springer series in statistics, 2nd edn. Springer, New York","edition":"2"},{"issue":"7","key":"1135_CR10","first-page":"1","volume":"28","author":"LM Hou","year":"2011","unstructured":"Hou LM, Ma GF (2011) Forecast of railway passenger traffic based on a grey linear regression combined model. Comput Simulat 28(7):1\u20133 (30)","journal-title":"Comput Simulat"},{"key":"1135_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An introduction to statistical learning with applications in R","author":"G James","year":"2013","unstructured":"James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning with applications in R. Springer, New York"},{"issue":"2","key":"1135_CR12","doi-asserted-by":"publisher","first-page":"215","DOI":"10.14257\/ijhit.2016.9.2.19","volume":"9","author":"Y Jia","year":"2016","unstructured":"Jia Y, He P, Liu S, Cao L (2016) A combined forecasting model for passenger flow based on GM and ARMA. Int J Hybrid Inf Techn 9(2):215\u2013226","journal-title":"Int J Hybrid Inf Techn"},{"key":"1135_CR13","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.trc.2014.03.016","volume":"44","author":"X Jiang","year":"2014","unstructured":"Jiang X, Zhang L, Chen XM (2014) Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transp Res Part C Emerg Technol 44:110\u2013127","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1135_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/9717582","volume":"2016","author":"PP Jiao","year":"2016","unstructured":"Jiao PP, Li R, Sun T, Hou ZH, Ibrahim A (2016) Three revised kalman filtering models for short-term rail transit passenger flow prediction. Math Probl Eng 2016:1\u201310","journal-title":"Math Probl Eng"},{"issue":"1","key":"1135_CR15","first-page":"32","volume":"28","author":"JT Li","year":"2007","unstructured":"Li JT, Yang JF (2007) Prediction of Dalian station passenger volume based on RBF neural network. J Dalian Jiaotong Univ 28(1):32\u201334","journal-title":"J Dalian Jiaotong Univ"},{"key":"1135_CR16","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.trc.2017.02.005","volume":"77","author":"Y LI","year":"2017","unstructured":"Li Y, Wang XD, Sun S, Ma XL, Lu GQ (2017) Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks. Transp Res part C Emerg Technol 77:306\u2013328","journal-title":"Transp Res part C Emerg Technol"},{"key":"1135_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0892-2","author":"JQ Li","year":"2018","unstructured":"Li JQ, Liu L, Zhou MC, Yang JJ, Chen S, Liu HT, Wang Q, Pan H, Sun ZH, Tan F (2018) Feature selection and prediction of small-for-gestational-age infants. J Ambient Intell Human Comput. \n                    https:\/\/doi.org\/10.1007\/s12652-018-0892-2","journal-title":"J Ambient Intell Human Comput"},{"issue":"3","key":"1135_CR18","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18\u201322","journal-title":"R News"},{"key":"1135_CR19","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.trc.2017.08.001","volume":"84","author":"LJ Liu","year":"2017","unstructured":"Liu LJ, Chen RC (2017) A novel passenger flow prediction model using deep learning methods. Transp Res Part C Emerg Technol 84:74\u201391","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1135_CR20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.trc.2013.12.008","volume":"39","author":"Z Ma","year":"2014","unstructured":"Ma Z, Xing J, Mesbah M, Ferreira L (2014) Predicting short-term bus passenger demand using a pattern hybrid approach. Transp Res Part C Emerg Technol 39:148\u2013163","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1135_CR21","doi-asserted-by":"publisher","DOI":"10.3846\/16484142.2016.1139623","author":"M Milenkovi\u0107","year":"2016","unstructured":"Milenkovi\u0107 M, \u0160vadlenka L, Melichar V, Bojovi\u0107 N, Avramovi\u0107 Z (2016) SARIMA modeling approach for railway passenger flow forecasting. Transp. \n                    https:\/\/doi.org\/10.3846\/16484142.2016.1139623","journal-title":"Transp"},{"key":"1135_CR22","unstructured":"Ministry of transport (2015) 7 reasons to love public transport in 2015 [Online]. \n                    https:\/\/www.mot.gov.sg\/Transport-Matters\/Public-Transport\/7-reasons-to-love-public-transport-in-2015\/\n                    \n                  . Accessed 2 Jun 2015"},{"issue":"3","key":"1135_CR23","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, Gama J, Ferreira M, Mendes-Moreira J, Damas L (2013) Predicting taxi-passenger demand using streaming data. IEEE Trans Intell Transp Syst 14(3):1393\u20131402","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"6","key":"1135_CR24","first-page":"1623","volume":"18","author":"M Ni","year":"2017","unstructured":"Ni M, He Q, Gao J (2017) Forecasting the subway passenger flow under event occurrences with social media. IEEE Trans Intell Transp Syst 18(6):1623\u20131632","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1135_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2815155","author":"S Pirbhulal","year":"2018","unstructured":"Pirbhulal S, Zhang H, Wu WQ, Mukhopadhyay SC, Zhang YT (2018) Heart-beats based biometric random binary sequences generation to secure wireless body sensor networks. IEEE Trans Biomed Eng. \n                    https:\/\/doi.org\/10.1109\/TBME.2018.2815155","journal-title":"IEEE Trans Biomed Eng"},{"key":"1135_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-017-0506-4","author":"Y Su","year":"2017","unstructured":"Su Y, Sun W (2017) Dynamic differential models for studying traffic flow and density. J Ambient Intell Human Comput. \n                    https:\/\/doi.org\/10.1007\/s12652-017-0506-4","journal-title":"J Ambient Intell Human Comput"},{"key":"1135_CR27","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.neucom.2015.03.085","volume":"166","author":"YX Sun","year":"2015","unstructured":"Sun YX, Leng B, Guan W (2015) A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomput 166:109\u2013121","journal-title":"Neurocomput"},{"issue":"6","key":"1135_CR28","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947\u20131958","journal-title":"J Chem Inf Comput Sci"},{"key":"1135_CR29","unstructured":"Taipei open government (2017) Inbound and outbound station-level passenger flow in Taipei metro in 2015 and 2016 [Online]. \n                    http:\/\/data.taipei\/opendata\/datalist\/datasetMeta?oid=1d71c478-205f-42c5-8386-35f86d74fdd1\n                    \n                  . Accessed 26 May 2017"},{"key":"1135_CR30","unstructured":"Taipei Rapid Transit Corporation (2017) Size of Taipei Metro [Online]. http:\/\/english.metro.taipei\/ct.asp?xItem=1315555&ctNode=70214&mp=122036. Accessed 4 May 2017"},{"issue":"2","key":"1135_CR31","doi-asserted-by":"publisher","first-page":"3728","DOI":"10.1016\/j.eswa.2008.02.071","volume":"36","author":"TH Tsai","year":"2009","unstructured":"Tsai TH, Lee CK, Wei CH (2009) Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Syst Appl 36(2):3728\u20133736","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1135_CR32","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.trc.2011.06.009","volume":"21","author":"Y Wei","year":"2012","unstructured":"Wei Y, Chen MC (2012) Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp Res part C Emerg Technol 21(1):148\u2013162","journal-title":"Transp Res part C Emerg Technol"},{"key":"1135_CR33","unstructured":"Wikipedia (2018a) Taichung City Bus [Online]. \n                    https:\/\/en.wikipedia.org\/wiki\/Taichung_City_Bus\n                    \n                  . Accessed 2 Sep 2018"},{"key":"1135_CR34","unstructured":"Wikipedia (2018b) Overfitting [Online]. \n                    https:\/\/en.wikipedia.org\/wiki\/Overfitting\n                    \n                  . Accessed 15 Oct 2018"},{"key":"1135_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2832069","author":"WQ Wu","year":"2018","unstructured":"Wu WQ, Pirbhulal S, Zhang H, Mukhopadhyay SC (2018) Quantitative assessment for self-tracking of acute stress based on triangulation principle in wearable sensor system. IEEE J Biomed Health. \n                    https:\/\/doi.org\/10.1109\/JBHI.2018.2832069","journal-title":"IEEE J Biomed Health"},{"key":"1135_CR36","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.jairtraman.2014.01.009","volume":"37","author":"G Xie","year":"2014","unstructured":"Xie G, Wang S, Lai KK (2014) Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches. J Air Transp Manag 37:20\u201326","journal-title":"J Air Transp Manag"},{"key":"1135_CR37","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.trc.2015.02.019","volume":"58","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Haghani A (2015) A gradient boosting method to improve travel time prediction. Transp Res part C Emerg Technol 58:308\u2013324","journal-title":"Transp Res part C Emerg Technol"},{"issue":"4","key":"1135_CR38","first-page":"154","volume":"11","author":"CH Zhang","year":"2011","unstructured":"Zhang CH, Song R, Sun Y (2011) Kalman filter-based short-term passenger flow forecasting on bus stop. J Trans Syst Eng Inf Technol 11(4):154\u2013159","journal-title":"J Trans Syst Eng Inf Technol"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-018-1135-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12652-018-1135-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-018-1135-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T19:24:45Z","timestamp":1573932285000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12652-018-1135-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,17]]},"references-count":38,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2019,11]]}},"alternative-id":["1135"],"URL":"https:\/\/doi.org\/10.1007\/s12652-018-1135-2","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,17]]},"assertion":[{"value":"30 April 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}