{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:33:03Z","timestamp":1760239983582,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,15]],"date-time":"2019-02-15T00:00:00Z","timestamp":1550188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese National Natural Science Fund","award":["61703064"],"award-info":[{"award-number":["61703064"]}]},{"DOI":"10.13039\/501100013223","name":"Chongqing Research Program of Basic Research and Frontier Technology","doi-asserted-by":"publisher","award":["cstc2017jcyjAX0473"],"award-info":[{"award-number":["cstc2017jcyjAX0473"]}],"id":[{"id":"10.13039\/501100013223","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund Project of Chongqing Key Laboratory of Traffic &amp; Transportation","award":["2019CQJY001"],"award-info":[{"award-number":["2019CQJY001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.<\/jats:p>","DOI":"10.3390\/a12020038","type":"journal-article","created":{"date-parts":[[2019,2,17]],"date-time":"2019-02-17T22:11:50Z","timestamp":1550441510000},"page":"38","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6946-2367","authenticated-orcid":false,"given":"Zhihong","family":"Yao","sequence":"first","affiliation":[{"name":"Chongqing Key Laboration of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China"},{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA"}]},{"given":"Yibing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Architecture Engineering, Yantai Vocational College, Yantai 264670, China"}]},{"given":"Wei","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Bin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Bo","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,15]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Traffic Signal Settings","volume":"39","author":"Webster","year":"1958","journal-title":"Lond. 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