# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 3 ETC Data-Based Traffic Sensing and Feature Extraction
### ETC Data Structure and Characteristic Analysis
#### 3.1.2 Data Quality Control  
Although ETC data possesses significant advantages, such as high precision and strong real-time characteristics, comprehensive data quality control systems must still be established in practical applications to ensure data accuracy, completeness, and consistency. Data quality control is not only a technical issue but also a critical component affecting the reliability of the entire traffic sensing system.  
The systematic development of data cleaning and preprocessing technologies reflects the maturity of ETC data applications. Data quality can be significantly improved through technical means, such as removing duplicate records, correcting temporal anomalies, and processing missing values. The establishment of anomaly data identification and processing mechanisms is crucial for ensuring ETC data reliability. By setting reasonable business rules and statistical thresholds, problematic data such as speed anomalies, time interval anomalies, and path anomalies can be effectively identified.  
The systematic development of data cleaning and preprocessing technologies reflects the maturity of ETC data applications. Data quality can be significantly improved through technical means such as removing duplicate records, correcting temporal anomalies, and processing missing values. [PERSON] et al. proposed systematic quality control methods based on ETC toll data for real-time expressway traffic data imputation and state prediction, employing low-rank tensor decomposition theory to repair missing data [17]. Their approach utilized confusion matrices to determine the thresholds for identifying missing data and introduced three-dimensional tensor models with low-rank tensor completion methods for missing data repair. Compared with traditional Lagrangian interpolation methods, K-nearest neighbor algorithms, and support vector regression methods, their method demonstrated optimal data repair effectiveness, providing important technical support for ETC data quality control.  
The establishment of anomaly data identification and processing mechanisms is crucial for ensuring ETC data reliability. Through classifying and identifying missing data types, systematic data repair approaches can achieve significant improvements in data quality. By setting reasonable business rules and statistical thresholds, problematic data such as speed anomalies, time interval anomalies, and path anomalies can be effectively identified through automated systems.  
The construction of data integrity verification systems ensures the logical consistency of ETC data. By checking the matching of vehicle entry and exit records, verifying temporal sequence continuity, and validating accounting information accuracy, multi-level data verification mechanisms have been established. This system not only identifies data problems but also provides a basis for data repair, thereby ensuring transaction record completeness and traceability.  
From a technological development perspective, ETC data quality control is evolving in intelligent and automated directions. Applications of emerging technologies, such as machine learning-based anomaly detection algorithms, deep learning-based data repair methods, and knowledge graph-based consistency verification technologies, are continuously improving data quality control levels.