{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T02:13:18Z","timestamp":1769307198894,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,25]],"date-time":"2019-12-25T00:00:00Z","timestamp":1577232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development  Program of China","award":["2017YFB0503701"],"award-info":[{"award-number":["2017YFB0503701"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471325"],"award-info":[{"award-number":["41471325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific and Technological Leading Talent Fund of the  National Administration of Surveying, Mapping and Geo-information","award":["2014"],"award-info":[{"award-number":["2014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion; however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities.<\/jats:p>","DOI":"10.3390\/s20010150","type":"journal-article","created":{"date-parts":[[2019,12,25]],"date-time":"2019-12-25T11:07:48Z","timestamp":1577272068000},"page":"150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen"],"prefix":"10.3390","volume":"20","author":[{"given":"Zhenwei","family":"Luo","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2034-982X","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China"},{"name":"RE-Institute of Smart Perception and Intelligent Computing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"He","sequence":"additional","affiliation":[{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengming","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, 28 Lianghuachi West Road, Haidian Qu, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihong","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China"},{"name":"RE-Institute of Smart Perception and Intelligent Computing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8066-9203","authenticated-orcid":false,"given":"Shen","family":"Ying","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China"},{"name":"RE-Institute of Smart Perception and Intelligent Computing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliang","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.future.2015.11.013","article-title":"Urban traffic congestion estimation and prediction based on floating car trajectory data","volume":"61","author":"Kong","year":"2016","journal-title":"Future Gener. 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