# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation
### Analysis of Driving Primitives  
Figure 10 illustrates the driving behaviors in relation to various kinematic features, including lateral velocity (v_x), lateral acceleration (a_x), longitudinal velocity (v_y), and longitudinal acceleration (a_y), alongside the velocity differential (\(\Delta\)V). The plots represent the temporal changes in each feature, labeled with different driving primitives identified by the Sparse Inverse Covariance Clustering (SICC-SC) method. The color-coded labels correspond to the driving primitives depicted in Figure 1, providing a clear visual association of each primitive with specific kinematic behaviors. The variation in lateral and longitudinal velocities and accelerations highlights the influence of different driving primitives over time.  
Figure 11 presents the distribution of driving primitives extracted using the SICC-SC method. The color map represents the frequency of each driving primitive, labeled from #1 to #5, with corresponding normalized occurrence proportions. Notably, primitive #1 (yellow) accounts for 28% of the overall driving behavior, reflecting consistent behaviors such as stable high-speed driving. Primitives #3 (green) and #4 (cyan) represent 22% and 17%, respectively, corresponding to low-speed operations and moderate acceleration. Primitive #2 (blue), contributing to 15% of the driving instances, characterizes stopping or idling behavior, indicating a significant share of routine traffic stops. Primitive #5 (cyan) reflects sharp acceleration, accounting for 18%, which may be associated with overtaking or rapid lane changes. Overall, the analysis suggests that a considerable portion of the driving behavior involves regular high-speed cruising, low-speed control, and frequent stopping maneuvers, illustrating a diverse mixture of urban and express  
Figure 9: Spatial attention shift during lane change for vehicle 1585, where blue and purple means the car is changing lanes, and green means the car is unchanged in the current lane.  
It is evident that each driving primitive demonstrates distinct effects on the kinematic features. For instance, during periods labeled with primitive #1, the lateral and longitudinal velocities (v_x and v_y) exhibit stable trends, indicating cruising at consistent speeds. Conversely, during periods associated with primitive #5, characterized by rapid acceleration, spikes in both longitudinal velocity and acceleration are observed. The presence of primitive #3 (low-speed driving) corresponds with lower values of both v_y and a_y, suggesting careful driving in high-traffic areas.  
The bottom panel of Figure 11 further demonstrates the segmentation of driving primitives over time, visualized as a color-coded bar. This segmentation highlights the temporal dynamics of each primitive throughout the driving period, effectively summarizing the diversity in driving styles and the frequent shifts in driving behavior. The frequent changes between primitives #1, #3, and #5 indicate a dynamic driving pattern, with the driver consistently alternating between high-speed cruising, low-speed maneuvering, and aggressive accelerations, reflecting the complexity of real-world traffic scenarios.