Affiliation:
1. Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China
2. Fujian Provincial Expressway Information Technology Co., Ltd., Fuzhou 350011, China
Abstract
China’s widely adopted expressway ETC system provides a feasible foundation for realizing co-operative vehicle–infrastructure integration, and the accuracy of ETC data, which forms the basis of this scheme, will directly affect the safety of driving. Therefore, this study focuses on the abnormal data in an expressway ETC system. This study combines road network topology data and capture data to mine the abnormal patterns of ETC data, and it designs an abnormal identification model for expressway transaction data based on TL-XGBoost. This model categorizes expressway ETC abnormal data into four distinct classes: missing detections, opposite lane detection, duplicated detection and reverse trajectory detection. ETC transaction data from a southeastern Chinese province were used for experimentation. The results validate the model’s effectiveness, achieving an accuracy of 98.14%, a precision of 97.59%, a recall of 95.44%, and an F1-score of 96.49%. Furthermore, this study conducts an analysis and offers insights into the potential causes of anomalies in expressway ETC data.
Funder
Key Technologies Innovation and Industrialization Project
Renewable Energy Technology Research institution of Fujan University of Technology Ningde, China
2020 Fujian Province “Belt and Road” Technology Innovation Platform
The Construction Project of the Intelligent Networking Research Institute of Fujian University of Engineering
Provincial Candidates for the Hundred, Thousand and Ten Thousand Talent of Fujian
Patent Grant project
Horizontal projects
Municipal level science and technology projects
Fujian Provincial Department of Science and Technology Foreign Cooperation Project
Open Fund project
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