## 4 Experiments and Results Analysis  
### Data Sources and Preprocessing  
The data used in this study come from urban road travel time data in Shenzhen, China. The dataset encompasses travel time information from Shenzhen's urban road network spanning from March 1 to May 31 providing comprehensive coverage of both weekdays and weekends across different traffic conditions. The data collection system integrates multiple sources typical of modern intelligent transportation systems, including probe vehicle GPS tracking and fixed detection equipment deployed throughout Shenzhen's urban road network. Each travel time measurement represents the average travel time of vehicles entering a specific road segment during 2 min intervals, calculated from real-time traffic monitoring systems. The processed dataset contains 66,240 data points across 132 road segments, with each segment characterized by geometric attributes including length (ranging from several hundred meters to over 2 km), width specifications, and road-type classifications. The temporal coverage spans 92 days of continuous monitoring, capturing diverse traffic patterns including peak hours, off-peak periods, weekends, and various weather conditions typical of Shenzhen's subtropical climate. The dataset was partitioned following temporal order to prevent data leakage: shallow learning models used 80%/20% train/test split, while deep learning models employed a 60%/20%/20% train/validation/test split. Each deep learning model underwent 5 independent training runs with different random seeds to ensure statistical robustness. Detailed data characteristics and processing: The original dataset comprises three interconnected components that collectively provide comprehensive urban road network information. The first component contains road segment geometric and operational attributes, including unique segment identifiers, physical dimensions (length and width measurements), and road classification categories. The second component establishes the topological relationships between road segments, documenting the direct upstream and downstream connectivity patterns essential for understanding traffic flow propagation. The third component records the temporal travel time measurements, capturing average travel times for each road segment at 2 min intervals throughout the monitoring period. Following systematic analysis of data completeness across all available road segments, we selected segment A102 as our primary research subject based on its minimal missing data ratio during peak traffic periods. This 247 m segment with a 12 m unidirectional width demonstrates representative urban road characteristics while ensuring data quality for reliable model training. The travel time distribution for this segment exhibits typical urban traffic patterns, with 75% of observations concentrated within the 25-32 s range, while extreme values reach approximately 10 times the normal travel times, reflecting the significant impact of traffic signals and non-recurrent events on urban road performance. Missing data points were identified primarily during nighttime low-traffic periods (3:00-5:00 AM) and addressed through forward-filling preprocessing, where each missing value was replaced with the most recent valid measurement to maintain temporal continuity in the dataset. As shown in Figure 2, the raw data underwent encoding and analysis processing, adding temporal features (date, recording time, weekday encoding, etc.) and calculating average segment speeds based on segment length and average travel time.