# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 5 ETC-Based Urban Traffic Management: Applications and Practices  
### Real-Time Monitoring and Signal Control Optimization  
Real-time monitoring systems based on ETC data have been implemented in multiple cities. [PERSON] investigated the comprehensive applications of ETC gantry big data based on cloud computing, proposing a two-tier cloud computing architecture of \"edge cloud + central cloud.\" This architecture leverages the virtualization, large-scale integration, high reliability, and high scalability characteristics of cloud computing technology to achieve the asset-based management of ETC gantry data for comprehensive solutions, including precise billing, vehicle-road cooperation, thematic analysis, traffic modeling, real-time monitoring systems, and intelligent operation and maintenance systems [26]. This architecture provides robust technical support for urban traffic real-time monitoring, enabling second-level traffic state updates and responses. In signal control optimization, [PERSON] et al. proposed real-time adaptive signal control methods for connected vehicle environments, solving phase sequences and durations through two-level optimization problems, considering two objective functions: total vehicle delay minimization and queue length minimization. Research demonstrates that under high-penetration conditions, this control algorithm can reduce the total delay by 16.33% compared to traditional actuated control, while maintaining similar delay levels under low-penetration conditions [27]. [PERSON] investigated ETC-based traffic flow collection methods and intersection cooperative control models, proposing a traffic model for adjacent intersection cooperative control (ICC model). This model combines the remaining green time from upstream and downstream intersections, considers multiple signal states when vehicles arrive, and significantly improves lane vehicle traffic efficiency through the reasonable matching and setting of phase differences between adjacent signalized intersections [28]. [PERSON] et al. developed PAMSCOD, a queue-based arterial multi-modal signal control system that executes arterial traffic signal control in vehicle-to-infrastructure communication environments while considering multiple travel modes. This system can significantly reduce delays for both transit and automobiles under both saturated and oversaturated traffic conditions [29]. Regarding signal control in intelligent connected vehicle environments, [PERSON] and [PERSON] conducted a comprehensive review of cooperative intersection management, discussing major technologies and solutions for both signalized and non-signalized intersections, providing technology roadmaps for the future development of automated and cooperative intersections [30]. [PERSON] et al. systematically reviewed urban traffic signal control in connected and automated vehicle environments, reviewing the methods and models for estimating traffic flow states and optimizing traffic signal timing schemes based on CAV. They classified CAV-based traffic control methods into six types and proposed conceptual mathematical frameworks that can be specified for each method through selecting different state variables, control inputs, and environmental inputs [31]. [PERSON] et al. further proposed joint optimization methods for vehicle trajectories and intersection controllers for connected automated vehicles, employing algorithms combining dynamic programming and shooting heuristics (DP-SH). This approach can simultaneously optimize CAV trajectories and intersection controllers (traffic signal timing and phasing), reducing average travel time by 35.72% and fuel consumption by 31.5% compared to adaptive signal control [32]. These studies demonstrate that ETC data-based real-time monitoring and signal control optimization technologies have transitioned from traditional passive-response modes to proactive-prediction and intelligent-control modes.