{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:15:56Z","timestamp":1777889756612,"version":"3.51.4"},"reference-count":29,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2020YFB1600703"],"award-info":[{"award-number":["2020YFB1600703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2023,3,3]]},"abstract":"<jats:p>Accurately predicting passenger flow at rail stations is an effective way to reduce operation and maintenance costs, improve the quality of passenger travel while meeting future passenger travel demand. The improvement of data acquisition capability allows fine-grained and large-scale built environment data to be extracted. Therefore, this paper focuses on investigating the relationship between the built environment around the station and the station passenger flow and discusses whether the built environment data can be applied to the station passenger flow prediction. Firstly, the evaluation system of station passenger flow influencing factors is built based on multisource data. The inner relationship between built environment factors and station passenger flow is investigated using the Pearson correlation analysis. Based on this, a multilayer perceptron (MLP)-based passenger flow prediction model was developed to predict the passenger flow at key stations. The study results show that the built environment factors impact station passenger flow, and the MLP prediction model has better prediction accuracy and applicability. The results of the study can be applied to predict the passenger flow scale of rail stations without historical passenger flow data and thus are also applicable to new rail stations.<\/jats:p>","DOI":"10.1155\/2023\/1430449","type":"journal-article","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T20:05:06Z","timestamp":1677873906000},"page":"1-19","source":"Crossref","is-referenced-by-count":11,"title":["Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)"],"prefix":"10.1155","volume":"2023","author":[{"given":"Luzhou","family":"Lin","sequence":"first","affiliation":[{"name":"School of Economics, Peking University, Yiheyuan Rd. 5, 100871 Beijing, China"},{"name":"Quantutong Location Network Co., Ltd, No. 2, Liangshuihe 1st Street, Beijing Economic and Technological Development Zone, Beijing 100163, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4998-5563","authenticated-orcid":true,"given":"Yuezhe","family":"Gao","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingxin","family":"Cao","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zifan","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cai","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Huajin Campus, South 189 Jiuhua Rd, Wuhu 241002, China"},{"name":"Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2008.11.002"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2011.05.004"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2014.12.132"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2011.04.016"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2012.04.116"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2018.04.006"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jairtraman.2018.04.001"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/s0965-8564(99)00043-9"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1080\/01944360608976751"},{"key":"10","doi-asserted-by":"crossref","DOI":"10.1002\/9781119993308","volume-title":"Modelling Transport","author":"J. de Dios Ort\u00fazar","year":"2011"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2014.08.021"},{"key":"12","article-title":"New Method for Transit Ridership Forecasting","author":"G. 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