# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 4 Traffic State Analysis and Assessment Using ETC Data
### Cross-Section Traffic State Analysis  
Cross-sections serve as fundamental units for traffic flow analysis, and the accuracy of their state analysis directly affects the effectiveness of the entire traffic management system. ETC data-based cross-section traffic state analysis can not only replace traditional methods but more importantly achieves improvements in analysis precision, timeliness, and scalability. These technological advantages stem from the structured characteristics and high-quality assurance of ETC data, providing a solid foundation for constructing intelligent traffic state analysis systems.  
Clustering-based cross-section classification methods embody the core concept of data-driven traffic analysis. By utilizing clustering algorithms such as K-means and fuzzy C-means to classify cross-sections based on characteristics including flow, speed, and occupancy, the objective quantification and scientific classification of traffic states can be achieved. [PERSON] et al. proposed travel pattern analysis and differentiated pricing strategies based on highway transaction data, employing self-organizing mapping algorithms to classify user characteristics [22]. The innovation of this research lies not only in focusing on the physical characteristics of traffic flow but also in deeply exploring user behavioral characteristics, providing theoretical foundations for personalized traffic management.  
[PERSON] et al. investigated ETC data-based methods for classifying passenger car commuting and tourism groups [23]. This research employed data analysis methods, including association analysis and cluster analysis, to conduct the multi-faceted characteristic and threshold analyses of user groups, constructing a systematic process framework for distinguishing passenger car commuting groups from tourism groups. The significant contribution of this research lies in organically combining traffic state analysis with user behavior analysis, providing technical support for precise traffic services. The research validated the effectiveness of the classification methods, demonstrating that the adopted analytical approaches can accurately explore the potential and value of passenger car commuting and tourism groups, offering theoretical support for ETC enterprise business expansion. This traffic state analysis method, based on user group characteristics, not only improves analysis specificity and accuracy but also provides scientific foundations for formulating differentiated traffic management strategies.  
[PERSON] et al. proposed fatigue driving identification methods for ETC intelligent systems [24]. This research addressed the driving traffic safety behaviors of fatigued drivers by proposing application research on fatigue driving identification methods based on ETC intelligent systems at service areas. Building upon the full utilization of existing ETC gantry infrastructure, the research achieved the automatic collection and analysis of vehicle driving records through deploying ETC antennas at highway service area entrances and exits, providing warning information to ETC vehicles traveling on highways. The innovation of this research lies in organically combining traffic safety management with ETC systems, improving the accuracy and timeliness of traffic safety warnings through intelligent and informatized technology applications. The cloud platform analyzes vehicle driving records from vehicle passage data uploaded by ETC antennas, thereby achieving the intelligent identification and proactive warning of fatigue driving behaviors. This technical approach not only improves traffic safety management efficiency but also provides referenceable technical pathways for similar intelligent applications.