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Firstly, the characteristics of the ETC gantry data are analyzed, and then data are cleaned and reconstructed, after which an algorithm is proposed for constructing a vehicle travel speed data set. Secondly, the speed feature vector model of the road section is established by taking the relationship among the speed distribution feature, time domain feature, and the maximum speed limit of the road section into consideration. Then, a data supplement algorithm is constructed to solve the problem of the imbalance of data samples. Finally, the combined GC-XGBoost classification algorithm is used to train and learn the potential speed limit features, and it is verified through the Fujian Provincial Expressway ETC data and the speed limit information provided by the Fujian Traffic Police. The result shows that the accuracy of the method in the recognition of the maximum limited speed information of the expressway is 97.5%. Compared with the traditional limited speed information recognition and extraction methods, the proposed approach can identify the maximum limited speed information of each section of the expressway more efficiently. It can also accurately identify the dynamic change of the maximum limited speed information, which is able to provide data support for intelligent expressway management systems and map providers.<\/jats:p>","DOI":"10.1155\/2021\/4702669","type":"journal-article","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T20:05:17Z","timestamp":1637265917000},"page":"1-15","source":"Crossref","is-referenced-by-count":5,"title":["The Method of Dynamic Identification of the Maximum Speed Limit of Expressway Based on Electronic Toll Collection Data"],"prefix":"10.1155","volume":"2021","author":[{"given":"Fumin","family":"Zou","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, Fujian, China"}]},{"given":"Feng","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, Fujian, China"}]},{"given":"Junshan","family":"Tian","sequence":"additional","affiliation":[{"name":"Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, Fujian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7726-7297","authenticated-orcid":true,"given":"Sijie","family":"Luo","sequence":"additional","affiliation":[{"name":"Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, Fujian, China"}]},{"given":"Xiang","family":"Yu","sequence":"additional","affiliation":[{"name":"Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, Fujian, China"}]},{"given":"Qing","family":"Gu","sequence":"additional","affiliation":[{"name":"Fujian Provincial Expressway Information Technology Co., Ltd., Fuzhou 350011, Fujian, China"}]},{"given":"Lyuchao","family":"Liao","sequence":"additional","affiliation":[{"name":"Fujian Provincial Big Data Research Institute of Intelligent Transportation, Fujian University of Technology, Fuzhou 350118, Fujian, China"}]}],"member":"311","reference":[{"key":"1","article-title":"Ministry of Transport of the People\u2019s Republic of China. 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