Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost
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 dat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/1/202 |
| _version_ | 1797358948290920448 |
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| author | Fumin Zou Rouyue Shi Yongyu Luo Zerong Hu Huan Zhong Weihai Wang |
| author_facet | Fumin Zou Rouyue Shi Yongyu Luo Zerong Hu Huan Zhong Weihai Wang |
| author_sort | Fumin Zou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T15:08:36Z |
| format | Article |
| id | doaj.art-978ac2b8b2034f75ba6e8ede74dc13c6 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T15:08:36Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-978ac2b8b2034f75ba6e8ede74dc13c62024-01-10T14:55:09ZengMDPI AGElectronics2079-92922024-01-0113120210.3390/electronics13010202Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoostFumin Zou0Rouyue Shi1Yongyu Luo2Zerong Hu3Huan Zhong4Weihai Wang5Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaFujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaFujian Provincial Expressway Information Technology Co., Ltd., Fuzhou 350011, ChinaFujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaFujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaFujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaChina’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.https://www.mdpi.com/2079-9292/13/1/202abnormal dataexpresswaycooperative vehicle-infrastructurepattern analysis |
| spellingShingle | Fumin Zou Rouyue Shi Yongyu Luo Zerong Hu Huan Zhong Weihai Wang Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost Electronics abnormal data expressway cooperative vehicle-infrastructure pattern analysis |
| title | Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost |
| title_full | Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost |
| title_fullStr | Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost |
| title_full_unstemmed | Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost |
| title_short | Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost |
| title_sort | expressway etc transaction data anomaly detection based on tl xgboost |
| topic | abnormal data expressway cooperative vehicle-infrastructure pattern analysis |
| url | https://www.mdpi.com/2079-9292/13/1/202 |
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