# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
### Literature Search Methodology  
This comprehensive narrative review employed a multi-database literature search to identify the relevant studies on ETC data-based urban traffic state sensing and analysis technologies. The primary databases searched included Web of Science Core Collection, IEEE Xplore Digital Library, ScienceDirect, Transportation Research International Documentation (TRID), and Engineering Index (EI Compendex) to ensure the comprehensive coverage of the international literature in transportation engineering, computer science, and intelligent systems. Given the significant developments in ETC technology in Chinaand other regions, supplementary searches were conducted in regional databases, including China National Knowledge Infrastructure (CNKI) and relevant conference proceedings from major venues such as the IEEE Intelligent Transportation Systems Conference, Transportation Research Board Annual Meeting, and International Conference on Intelligent Transportation Systems.  
The search strategy utilized comprehensive combinations of key terms across multiple domains: ETC-related terms (\"Electronic Toll Collection,\" \"ETC system,\" \"DSRC,\" \"On-Board Unit,\" \"Road-Side Unit\"); traffic sensing terms (\"traffic state sensing,\" \"traffic flow detection,\" \"speed estimation,\" \"congestion detection,\" \"traffic monitoring\"); data analysis terms (\"data fusion,\" \"feature extraction,\" \"spatiotemporal analysis,\" \"machine learning,\" \"artificial intelligence,\" \" graph neural networks\"); urban applications (\"urban traffic management,\" \"intelligent transportation systems,\" \"smart transportation,\" \"traffic prediction,\" \"signal control optimization\"); and integration concepts (\"multi-source data,\" \"sensor fusion,\" \"big data analytics,\" \"edge computing,\" \"vehicle-road cooperation\"). Boolean operators (AND, OR) were employed to create targeted search strings, with language filters applied to include studies in English and Chinese.  
The temporal scope primarily focused on publications from 2010 to 2025 to capture the evolution from basic ETC toll collection systems to advanced urban traffic management applications, while foundational earlier works (1998-2009) were selectively included when historically significant for understanding ETC technology development trajectory.  
The literature selection process involved multiple stages: (1) initial automated screening based on title relevance and abstract content; (2) full-text review for studies meeting inclusion criteria of ETC technology application in urban traffic contexts; (3) quality assessment focusing on peer-reviewed publications, conference papers from recognized venues, and technical reports from established research institutions; (4) backward and forward citation tracking to identify additional relevant studies. Inclusion criteria specifically targeted studies addressing ETC data processing, urban traffic state analysis, multi-source data fusion involving ETC, artificial intelligence applications in ETC systems, and practical implementations in urban environments. Studies focusing solely on highway applications without urban relevance, purely theoretical models without practical validation, and non-English/Chinese publications were excluded.  
The process emphasized thematic organization around key technical and application domains: (1) ETC system architecture and technology principles; (2) data processing and quality control methodologies; (3) traffic state feature extraction across microscopic, mesoscopic, and macroscopic levels; (4) multi-source data fusion frameworks and algorithms; (5) artificial intelligence and machine learning applications; (6) urban traffic management implementation cases; (7) emerging technologies and future integration directions. This organizational structure facilitates an understanding of both technological foundations and practical applications while identifying research gaps and future opportunities in ETC-based urban traffic management.