# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 3 ETC Data-Based Traffic Sensing and Feature Extraction
### Traffic State Feature Extraction Methods
#### 3.2.2 Mesoscopic Feature Extraction  
Mesoscopic feature extraction focuses on traffic states at the road segment and regional levels. Feature extraction at this level requires the comprehensive consideration of interactions among multiple microscopic elements, reflecting the dynamic characteristics and complexity of transportation systems.  
The accuracy of road segment congestion identification technology serves as a prerequisite for traffic management responses. [PERSON] et al. proposed recurrent traffic congestion management strategies for urban expressways integrated with ETC systems, analyzing the characteristics of recurrent traffic congestion on urban expressways [19]. Based on ETC system functions and ramp control principles, they proposed expressway ETC ramp control and ramp entrance-exit OD flow analysis models. Through determining and analyzing key ramp entrances and exits and their different causes of recurrent congestion, they developed corresponding congestion management strategies, providing important references for expressway recurrent congestion management and ramp control.  
The practicality of queue length estimation methods reflects the value of ETC data in refined traffic management. [PERSON] et al. developed methods for real-time queue length estimation at signalized intersections using travel times from mobile sensors [20]. Their approach was based on the observation that critical pattern changes in intersection travel times or delays, such as discontinuities (sudden and dramatic increases in travel times) and non-smoothness (changes in slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times, real-time queue lengths can be reconstructed. They introduced the concept of \"Queue Rear No-delay Arrival Time\" and demonstrated how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays, providing important support for signal control optimization.  
Traffic density analysis technology is based on the classical flow-speed-density fundamental relationship, deriving traffic density from flow and speed data. Although this method has a mature theoretical foundation, practical applications must consider the characteristics and limitations of ETC data. Unlike continuous detection data, ETC data possesses discrete characteristics, requiring appropriate interpolation and fitting methods to estimate continuous density distributions. Additionally, factors such as equivalent conversion for different vehicle types, the effects of mixed traffic flows, and the representativeness of detection cross-sections must be considered.  
Development trends in mesoscopic feature extraction are reflected in algorithmic intelligence, real-time processing, and result precision. The applications of new methods such as machine learning-based congestion pattern recognition, deep learning-based traffic state prediction, and reinforcement learning-based dynamic threshold optimization are continuously advancing mesoscopic feature extraction technologies.  
To provide a comprehensive comparison of different feature extraction approaches, Table 2 presents a systematic comparison of ETC data-based traffic state feature extraction methods across microscopic, mesoscopic, and macroscopic levels. This comparison highlights the strengths, limitations, and applicability of various approaches, enabling researchers and practitioners to select the most appropriate methods for their specific applications.