## 3 Experimental Design and Implementation
### Experimental Data
#### 3.1.1 Data Scale and Temporal Scope  
The experimental data originates from ride-hailing vehicle trajectory data collected from an actual urban road network floating car trajectory system. The ride-hailing vehicle data represents typical urban passenger transportation patterns during morning peak hours.  
The original trajectory data contains 2,487,406 records, covering data from 10 time periods. Each time period spans 2 h, specifically during weekday morning peak hours (7:00-9:00 a.m.). While the number of individual vehicles (7252 after preprocessing) may appear modest compared to citywide traffic volumes, the dataset represents large-scale trajectory data from multiple perspectives: (1) the substantial number of trajectory records (2.48 million GPS points) provides comprehensive spatiotemporal coverage, (2) the data originates from real-world ride-hailing operations representing actual urban travel patterns, and (3) the methodology framework is designed for scalability to accommodate larger datasets as connected vehicle penetration increases. The current dataset scale is sufficient for validating the proposed methodology and demonstrates its effectiveness for practical signal control applications in areas with limited traditional detection infrastructure.  
This temporal distribution characteristic makes the data particularly suitable for morning peak signal control optimization research, as morning peak hours represent the period of greatest urban traffic pressure and most urgent signal optimization needs.  
From a temporal span perspective, the data covers 10 consecutive weekdays. This span ensures data representativeness while avoiding traffic pattern changes due to excessively long time periods. The data volume distribution across time periods is relatively uniform, with vehicle counts ranging from 515 to 568 vehicles per period. This relatively stable distribution facilitates comparative analysis between time periods.  
The GPS trajectory data exhibits high temporal resolution with predominantly 3-s sampling intervals, ensuring accurate trajectory analysis. The high-frequency sampling (primarily 3-s intervals) provides sufficient granularity for accurate intersection passage detection and speed calculation, which is essential for reliable traffic parameter extraction. This temporal resolution is significantly higher than traditional detector-based systems and enables precise reconstruction of vehicle movement patterns through the urban road network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function Name** & **Main Input Parameters** & **Output Parameters** & **Description** \\ \hline makePolygon2 & inter\_origin; gon\_radius; gon\_nvert & gonxy & Given a set of centers, circumscribed circle radii, and corresponding regular polygon sides, outputs a set of regular polygon coordinates \\ \hline plotRoad & traj: inter\_origin & roadfig & Road network visualization function; besides this application scenario, inputting more parameters can satisfy various visualization requirements \\ \hline nodeDigger & traj: gonxy & node & Input trajectory data and regular polygon coordinates, output regular polygon numbers corresponding to trajectory points \\ \hline routeInfer & node, vinfo, mode; inter\_origin & route2, route3 & Output various vehicle path data \\ \hline routeDiagnose & route3, mode, route2, gon\_radius, route\_dialog & route\_dialog & Input vehicle path data and content requiring diagnosis or route_dialog (mode), output required features or data \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters and functions of sub-functions in the main vehicle path feature extraction function routeInferMain().