# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
###### Abstract  
Urban traffic management faces challenges, including inadequate sensing capabilities and insufficient operational status evaluation. The rapid expansion of electronic toll collection (ETC) systems from highways to urban roads provides new opportunities to address these issues. The vast amount of \"dormant\" ETC data contains rich traffic information that urgently needs to be deeply mined and effectively utilized. This paper reviews the research status, key technologies, and development trends of urban traffic state sensing and analysis technologies based on ETC data. In terms of technological development, ETC systems have evolved from simple toll collection tools to comprehensive traffic management platforms, featuring unique advantages such as accurate vehicle identification, extensive spatiotemporal coverage, and stable data quality. ETC data-based traffic sensing technologies encompass traffic state representation at microscopic, mesoscopic, and macroscopic levels, enabling comprehensive sensing from individual vehicle behavior to overall network operations. The construction of multi-source data fusion frameworks enables effective complementarity between ETC data, floating car data, and video detection data, significantly improving traffic state estimation accuracy. In practical applications, ETC data has demonstrated enormous potential in real-time monitoring and signal control optimization, traffic prediction and artificial intelligence technologies, environmental impact assessment, and other fields. Meanwhile, ETC data-based urban traffic management is transitioning from passive responses to proactive prediction, from single functions to comprehensive services, and from isolated systems to integrated platforms. Looking toward the future, the deep integration of emerging technologies, such as vehicle-road networking, edge computing, and artificial intelligence, with ETC systems will further promote the intelligent, refined, and precise development of urban traffic management.  
electronic toll collection; intelligent transportation systems; traffic state sensing; multi-source data fusion; urban traffic management +
Footnote †: journal: Applied sciences  
for Science and Technology released ten scientific questions with guiding significance for scientific development, with the sixth frontier scientific question explicitly asking: \"How can digital transportation infrastructure promote the development of autonomous driving and vehicle-road cooperation?\" ETC-based traffic sensing and analysis methods represent one of the important research directions established to address this question [1].  
With the continuous acceleration of urbanization, urban transportation worldwide is facing unprecedented challenges. Under such high-intensity traffic demand pressure, traditional traffic management and sensing methods have exposed numerous limitations. Existing traffic detection technologies mainly rely on loop detectors, video surveillance, microwave radar, and other equipment. Although these technologies perform well in specific scenarios, they generally suffer from limited coverage, high equipment maintenance costs, data quality susceptible to environmental influences, and difficulty in achieving large-scale continuous monitoring. Particularly in complex urban traffic environments, traditional sensing methods often fail to provide sufficiently accurate and timely traffic state information, affecting the scientific validity and effectiveness of traffic management decisions.  
In this context, the rapid development of electronic toll collection (ETC) systems has brought important opportunities for solving urban traffic sensing challenges. ETC systems, with their unique technological advantages, can achieve precise vehicle identification and data collection while vehicles pass at high speeds, featuring characteristics such as high identification accuracy, extensive spatiotemporal coverage, stable data quality, and relatively low operational costs.