## 4 Experimental Results and Analysis
### Visualization Analysis
#### 4.2.4 Traffic Parameter Volatility Analysis  
Traffic parameter volatility reflects traffic flow stability and serves as an important indicator for assessing link operational reliability. This study analyzed traffic parameter fluctuation characteristics by calculating standard deviations of average speeds and travel times for each link.  
The variance distribution of average speeds for each link (Figure 16) reveals spatial heterogeneity in speed fluctuations. Certain links exhibit significantly higher speed variances than others, indicating extremely unstable traffic conditions on these segments. High-volatility links often represent traffic bottlenecks, prone to cycles of congestion formation and dissipation. This instability imposes higher requirements on signal control, necessitating greater safety margins.  
The variance distribution of average travel times for each link (Figure 17) further confirms traffic flow instability. Links with high travel time variance largely coincide with those exhibiting high speed variance, forming \"vulnerable segments\" within the network. These links are sensitive to external disturbances, where minor perturbations may lead to substantial delay increases.  
Figure 15: Vehicle OD distribution.  
Volatility analysis results provide important guidance for signal control optimization. For high-volatility links, the following measures should be adopted: First, consider volatility in offset calculations, using mean values plus a certain proportion of standard deviation to determine offsets; second, implement adaptive control strategies, dynamically adjusting signal timing based on real-time traffic conditions; third, strengthen traffic flow stability management, reducing inflow fluctuations through measures such as upstream metering control.  
The average travel time diagram for each link (Figure 18) comprehensively illustrates the overall operational status of the road network. The diagram shows that the spatial  
Figure 16: Variance of average speeds for each link.  
Figure 17: Variance of average travel times for each link.  
distribution of travel times highly correlates with indicators such as stop density and speed fluctuations, validating the consistency of multi-dimensional parameter analysis. Links with longer travel times primarily concentrate along several key corridors, which should serve as priority areas for signal optimization.  
Through the above multi-perspective visualization analysis, this paper has comprehensively grasped the traffic operational characteristics of the road network. Stop density analysis identified congestion hotspots, flow parameter box plots revealed parameter distribution characteristics, flow direction characteristic analysis discovered tidal traffic patterns, OD distribution analysis clarified major traffic demands, and volatility analysis assessed operational reliability. These visualization results mutually validate and complement each other, collectively forming the data foundation for signal control optimization. Based on these analytical results, targeted optimization strategies can be formulated, including coordination control subarea division, offset optimization, split adjustment, and oversaturation control, ultimately achieving overall improvement in network operational efficiency.