{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:06:23Z","timestamp":1766732783043,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["2018YFB1600600","2022JKF434"],"award-info":[{"award-number":["2018YFB1600600","2022JKF434"]}]},{"name":"People\u2019s Public Security University of China Basic Scientific Research for New Teachers Starting Fund Project","award":["2018YFB1600600","2022JKF434"],"award-info":[{"award-number":["2018YFB1600600","2022JKF434"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Intelligent Vehicle\u2013Infrastructure Collaboration Systems (i-VICS) put forward higher requirements for the real-time security of dynamic traffic information interaction. It is difficult to ensure the safety of dynamic traffic information interaction by means of traditional static information security. In this study, a method was proposed through machine learning-based lane-changing (LC) behavior recognition and information credibility discrimination, based on the utilization and exploitation of traffic business characteristics. The method consisted of three stages: LC behavior recognition based on Support Vector Machine (SVM), LC speed prediction based on Recurrent Neural Network (RNN), and credibility discrimination of speed information under LC states. Firstly, the labeling rules of vehicle LC behavior and the input\/output of each stage model were determined, and the raw NGSIM data were processed to obtain data sets for LC behavior identification and LC speed prediction. Both the SVM classification and RNN prediction models were trained and tested, respectively. Afterwards, a model of credibility discrimination speed information under an LC state was constructed, and the real vehicle speed data were processed for model verification. The results showed that the overall accuracy of vehicle status recognition by the SVM model was 99.18%, and the precision of the RNN model was on the order of magnitude of cm\/s. Considering transverse and longitudinal abnormal velocity, the accuracy credibility discrimination of LC velocity was more than 97% in most experimental groups. The model can effectively identify the abnormal speed data of LC vehicles and provide support for the real-time identification of LC vehicle speed information under i-VICS.<\/jats:p>","DOI":"10.3390\/sym16010058","type":"journal-article","created":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T13:02:58Z","timestamp":1704114178000},"page":"58","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine Learning-Based Lane-Changing Behavior Recognition and Information Credibility Discrimination"],"prefix":"10.3390","volume":"16","author":[{"given":"Xing","family":"Chen","sequence":"first","affiliation":[{"name":"School of Traffic Management, People\u2019s Public Security University of China, Beijing 100038, China"}]},{"given":"Song","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Traffic Management, People\u2019s Public Security University of China, Beijing 100038, China"}]},{"given":"Jingsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Traffic Management, People\u2019s Public Security University of China, Beijing 100038, China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,1]]},"reference":[{"key":"ref_1","first-page":"40","article-title":"Technologies and Applications for Intelligent Vehicle-infrastructure Cooperation Systems","volume":"21","author":"Zhang","year":"2021","journal-title":"J. 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