# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 5 ETC-Based Urban Traffic Management: Applications and Practices
### Transformation of Highway Technology to Urban Applications  
Optimization technologies for highway ETC systems provide important technical references and application foundations for traffic management on urban expressways and arterial roads. Through appropriate adjustments and optimization, these technologies can be effectively applied in urban traffic environments. [PERSON] et al. proposed dynamic generation methods for highway ETC gantry topology based on LightGBM, using ETC gantry and toll station transaction data from a province in southeastern China to dynamically update ETC gantry topology data through machine learning algorithms. Comparisons with 14 other machine learning algorithms demonstrated that LightGBM performed optimally with 97.6% accuracy. This method can be directly applied to ETC gantry layout optimization for urban expressways [45]. [PERSON] et al. proposed data-driven optimization-based highway traffic state estimation methods, fusing ETC data with detector data and leveraging the extensive coverage provided by ETC data and fine-grained characteristics provided by detector data. By capturing probabilistic interdependencies between traffic state segments and their corresponding upstream and downstream segments, they developed two optimization models based on maximum likelihood and maximin likelihood principles. In case studies on the Zhejiang G92 Highway, the two optimization models achieved mean absolute percentage errors of 0.9% and 2.3%, respectively, during peak hours. This multi-source data fusion approach can be fully applied to traffic state estimation for urban expressways and arterial roads [46]. [PERSON] et al. proposed methods for dynamically determining toll plaza capacity through the real-time monitoring of approaching traffic conditions. Applications at San Francisco Bay Area toll plazas demonstrated that vehicle storage quantity estimates based on discharge rates and travel times can serve as proxy measures for predicting toll plaza capacity change effects. This dynamic capacity adjustment method is equally applicable to the management of urban toll road segments and expressway entrances [47]. In traffic demand understanding, [PERSON] et al. utilized electronic toll collection data to understand traffic demand, conducting dynamic origin-destination traffic volume estimation based on Hanshin Expressway ETC data. Research demonstrated that ETC data facilitates an understanding of traffic demand and its variations, clarifying the influences of external factors on OD flow changes and proposing statistical analysis methods for variations based on these factors. Results can be broadly applied to urban network management [48]. [PERSON] et al. investigated traffic information mining technologies based on ETC data, conducting a case analysis using Beijing's 2008 ETC data as examples. They proposed highway travel speed calculation models, traffic flow distribution analysis methods, and methods for obtaining indicators such as origin-destination distributions and vehicle-type proportion distributions. These data processing and extraction technologies are equally applicable to urban road traffic operations and planning [16]. [PERSON] and [PERSON] explored the spatiotemporal mobility of highway traffic flows based on ETC data for precise travel time estimation and prediction, proposing a travel time estimation and prediction (TTEP) framework. Through Weighted Root-Mean-Square Similarity (Weighted-RMSS) methods and Multi-Slope-Based Linear Regression (Multi-SBLR) methods, they improved the prediction accuracy for highway travel times. Experimental results demonstrated that this approach outperformed existing methods and could significantly reduce prediction errors for highway travel times. This framework can be applied to travel time prediction for urban arterial and expressways after appropriate adjustments [49]. In toll plaza management optimization, [PERSON] et al. analyzed the lane capacity of highway toll plazas using toll data.