# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 3 ETC Data-Based Traffic Sensing and Feature Extraction
### Multi-Source Data Fusion Framework  
Multi-source data fusion represents an advanced form of ETC data applications, embodying development trends in modern traffic sensing systems. The limitations of single data sources are increasingly apparent, while multi-source fusion can fully leverage the complementary advantages of different data sources, achieving simultaneous improvements in sensing precision and coverage. Multi-source fusion represents not only innovation in technical methods but also conceptual innovation in traffic sensing. Technical challenges in data-level fusion primarily manifest in data format standardization, spatiotemporal reference unification, and quality control consistency. Different data  
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sources employ different coordinate systems, temporal references, and precision standards, requiring effective data fusion through technical means such as coordinate transformation, temporal synchronization, and precision matching. Simultaneously, unified data quality evaluation systems must be established to ensure the reliability and consistency of the fused data. Key technologies in feature-level fusion include feature selection, weight allocation, and fusion algorithms. Feature selection involves identifying the most relevant traffic parameters from each data source, such as precise vehicle identification capabilities from ETC systems and continuous trajectory information from floating car data. Weight allocation determines the relative contribution of different data sources in the fusion process, requiring the consideration of each source's accuracy, coverage, and reliability characteristics. [PERSON] et al. conducted simulation-based evaluation research on traffic state estimation accuracy using floating car data in complex road networks [21]. Through 30 simulation experiments using VISSIM traffic simulation software and taking the microscopic simulation network of Shanghai's Luijazui area as an example, they analyzed the effects of floating car proportions and data sampling frequencies on network coverage and average travel speed estimation accuracy. Their results demonstrated that as floating car proportions and sampling frequencies increase, average travel speed estimation accuracy and network coverage gradually improve, with optimal results achieved when floating car proportions reach 8% and sampling frequencies reach 1/10 s. This research provides empirical guidance for determining optimal fusion parameters in multi-source data integration strategies. Fusion algorithms combine weighted features from different sources using various technical approaches, including statistical methods, machine learning techniques, and optimization algorithms. The effectiveness of fusion depends on proper algorithm selection based on data characteristics and application requirements. Factors such as feature timeliness, correlation, and independence must be considered to ensure the scientific validity and practicality of the fusion results. The research demonstrates that systematic approaches to multi-source data fusion can significantly improve traffic state estimation accuracy compared to single-source methods. Decision-level fusion strategies synthesize the analysis results from multi-source data, construct data view models, and achieve a comprehensive assessment of urban road traffic problems. Fusion at this level no longer focuses on specific data and features but on the analysis results and decision support. By establishing scientific decision fusion mechanisms, the advantages of different analysis methods can be fully utilized to improve the scientific validity and accuracy of decisions. The implementation of decision-level fusion requires the establishment of comprehensive knowledge bases and rule bases, constructing intelligent decision support systems through combinations of expert experience and machine learning.