{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T04:54:29Z","timestamp":1772081669099,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traffic congestion is a major problem in today\u2019s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion.<\/jats:p>","DOI":"10.3390\/s21082631","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T21:27:44Z","timestamp":1617917264000},"page":"2631","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm"],"prefix":"10.3390","volume":"21","author":[{"given":"Xiancheng","family":"Fu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Hengqiang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Hongjuan","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China"}]},{"given":"Zhihao","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2660-5492","authenticated-orcid":false,"given":"Weiming","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"ref_1","unstructured":"Dorigo, M. 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