{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T12:20:13Z","timestamp":1770034813271,"version":"3.49.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2021,11,4]]},"abstract":"<jats:p>With the development of the city, the passenger flow pressure of subway is increasing. At the same time, the daily travel of subway passengers produce a large amount of data such as inbound and outbound. How to use the big data to analyze the passenger flow is the key to solve the problems of crowded carriages and insufficient capacity in peak hours, high empty load rate in low hours and long waiting time for passengers. Based on the AFC data of Beijing subway system, this paper analyzes the temporal and spatial distribution characteristics of the passenger flow in the subway network. At the same time, taking subway line 5 as an example, it quantitatively calculates the imbalance coefficient of passenger flow in time, section and direction. Combined with the calculation results, it also proposes management optimization model and gives some adviceto the subway operation department from the aspects ofpassenger organization and transport management.<\/jats:p>","DOI":"10.3233\/jifs-189963","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T12:07:20Z","timestamp":1620994040000},"page":"4773-4783","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Subway passenger flow analysis and management optimization model based on AFC data"],"prefix":"10.1177","volume":"41","author":[{"given":"Jie","family":"Sun","sequence":"first","affiliation":[{"name":"Business College of Beijing Union University, Beijing, China"}]},{"given":"Jiajun","family":"Yao","sequence":"additional","affiliation":[{"name":"Business College of Beijing Union University, Beijing, China"}]},{"given":"Manxi","family":"Wang","sequence":"additional","affiliation":[{"name":"Business College of Beijing Union University, Beijing, China"}]}],"member":"179","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"LiuC. 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Research on Passenger Transport Organization Optimization of Urban Rail Transit Tianjin University of Technology (2019)."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2008.06.035"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.3141\/2417-04"},{"key":"e_1_3_2_10_2","first-page":"14","article-title":"Passenger Flow Analysis of Urban Rail Transit,(03)","volume":"2007","author":"Shen L.","unstructured":"ShenL., MaY. and GaoS., Passenger Flow Analysis of Urban Rail Transit,(03), Urban Transportation2007, 14\u201319.","journal-title":"Urban Transportation"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.2507\/IJSIMM16(3)2.380"},{"issue":"01","key":"e_1_3_2_12_2","first-page":"126","article-title":"Beijing Subway Station Type Identification based on Passenger Flow Characteristics","volume":"35","author":"Yin Q.","year":"2016","unstructured":"YinQ., MengB. and ZhangL., Beijing Subway Station Type Identification based on Passenger Flow Characteristics, Progress in Geographical Sciences35(01) (2016), 126\u2013134.","journal-title":"Progress in Geographical Sciences"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.05.012"},{"issue":"03","key":"e_1_3_2_14_2","first-page":"136","article-title":"Comparative Analysis of Passenger Flow Patterns of Beijing and London Subway based on AFC data","volume":"21","author":"Wang T.","year":"2018","unstructured":"WangT., ChenF., WangZ., ZhongC. and HuangJ., Comparative Analysis of Passenger Flow Patterns of Beijing and London Subway based on AFC data, Urban Rail Transit Research21 (03) (2018), 136\u2013141.","journal-title":"Urban Rail Transit Research"},{"key":"e_1_3_2_15_2","unstructured":"ZhangW. Research on Collaborative Passenger Flow Control of Urban Rail Transit Network Beijing Jiaotong University (2018)."},{"issue":"05","key":"e_1_3_2_16_2","first-page":"55","article-title":"Visual Analysis of Bus Passenger Flow based on IC card and GPS data","volume":"20","author":"Li W.","year":"2018","unstructured":"LiW., LinY., ChengY., LinX. and WangH., Visual Analysis of Bus Passenger Flow based on IC card and GPS data, Transportation Technology and Economy20 (05) (2018), 55\u201359+80.","journal-title":"Transportation Technology and Economy"},{"issue":"07","key":"e_1_3_2_17_2","first-page":"40","article-title":"Analysis of Subway Passenger Flow Characteristics and Optimization of Operation Measures","volume":"18","author":"Zhou Y.","year":"2018","unstructured":"ZhouY., Analysis of Subway Passenger Flow Characteristics and Optimization of Operation Measures, China Water Transport18(07) (2018), 40\u201342.","journal-title":"China Water Transport"},{"key":"e_1_3_2_18_2","unstructured":"ZhaoY. Research on Subway Train Scheduling Strategy Lanzhou Jiaotong University (2015)."},{"key":"e_1_3_2_19_2","unstructured":"ZhangY. Urban Rail Transit Operation Organization based on Short-term Passenger Flow Prediction Chang An University (2016)."},{"issue":"02","key":"e_1_3_2_20_2","first-page":"48","article-title":"Classification of subway stations based on card data and Gaussian mixture clustering","volume":"30","author":"Yue Z.","year":"2017","unstructured":"YueZ., ChenF., HuangJ. and WangB., Classification of subway stations based on card data and Gaussian mixture clustering, Urban Rapid Rail Transit30(02) (2017), 48\u201351+107.","journal-title":"Urban Rapid Rail Transit"},{"key":"e_1_3_2_21_2","unstructured":"LiuZ. Research on Optimization Model of Urban Rail Transit Train Operation Scheme based on Large and Small Routing Mode Beijing Jiaotong University (2019)."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189963","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-189963","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189963","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T01:57:46Z","timestamp":1769997466000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-189963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,11]]},"references-count":20,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,11,4]]}},"alternative-id":["10.3233\/JIFS-189963"],"URL":"https:\/\/doi.org\/10.3233\/jifs-189963","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,11]]}}}