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The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors can lead to unsafe interactions with surrounding vehicles, thereby increasing traffic risks. Therefore, solely focusing on individual vehicle perspectives and neglecting the influence of surrounding vehicles in abnormal lane-changing behavior detection has limitations. To address this, this study proposes a framework for abnormal lane-changing behavior detection. Initially, the study introduces a novel approach for representing vehicle trajectories that integrates information from surrounding vehicles. This facilitates the extraction of feature parameters considering the interactions between vehicles and distinguishing between different phases of lane-changing. The Light Gradient Boosting Machine (LGBM) algorithm is then employed to construct an abnormal lane-changing behavior detection model. The results indicate that this framework exhibits high detection accuracy, with the integration of surrounding vehicle information making a significant contribution to the detection outcomes.<\/jats:p>","DOI":"10.3390\/s23249800","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T12:00:37Z","timestamp":1702468837000},"page":"9800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection"],"prefix":"10.3390","volume":"23","author":[{"given":"Da","family":"Xu","sequence":"first","affiliation":[{"name":"Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China"},{"name":"Shandong Hi-Speed Group Innovation Research Institute, Jinan 250014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengfei","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Hi-Speed Group Innovation Research Institute, Jinan 250014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinpeng","family":"Yao","sequence":"additional","affiliation":[{"name":"Shandong Hi-Speed Group Innovation Research Institute, Jinan 250014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0926-9140","authenticated-orcid":false,"given":"Nengchao","family":"Lyu","sequence":"additional","affiliation":[{"name":"Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China"},{"name":"Engineering Research Center of Transportation Information and Safety, Ministry of Education, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104182","DOI":"10.1016\/j.trc.2023.104182","article-title":"Calibrating lane-changing models: Two data-related issues and a general method to extract appropriate data","volume":"152","author":"Ali","year":"2023","journal-title":"Transp. 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