# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation
### Results and Comparison  
In this chapter, the NGSIM US101 dataset is utilized to predict future vehicle trajectories using the HTSA-LSTM model alongside several baseline models. The performance of these models is evaluated using RMSE and NLL metrics. The baseline models assessed include:  
1. Constant Velocity (CV) [31]: An earlier trajectory prediction method based on physical principles, primarily using the vehicle's dynamic model and Kalman filter equations. Although the dataset lacks factors like road surface friction, making complex physics-based comparisons impractical, a simple physics-based model is constructed to predict the vehicle's trajectory under constant velocity. 2. Simple-LSTM (S-LSTM) [32]: This model uses LSTM units within a recurrent neural network to analyze continuous time-series data for temporal vehicle trajectory predictions. 3. Maneuver-LSTM (M-LSTM) [33]: Based on an encoder-decoder framework, this model uses historical trajectory data and lane structure, assigning confidence values to six maneuver categories to predict future motion through a multimodal distribution. 4. Attention-LSTM (A-LSTM) [3]: This model uses an LSTM encoder with shared weights to generate vector encodings of each vehicle's motion. An attention module models interactions based on the importance of adjacent vehicles, and the decoder uses these interaction vectors to predict trajectory distribution. 5. Convolutional Social Pooling LSTM (CS-LSTM) [27]: This model, based on an LSTM encoder-decoder framework, employs a social convolutional network to accurately capture spatial interactions among agents, producing multimodal trajectory predictions. 6. Spatial-Temporal Attention-LSTM(STA-LSTM) [1]: This model employs an LSTM encoder-decoder framework with integrated spatial and temporal attention mechanisms. It assigns weights to surrounding vehicles and historical time steps, capturing critical interactions for trajectory prediction. STA-LSTM enhances the accuracy of \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Input layer dimension of LSTM encoder & 32 \\ Dimension of LSTM hidden vectors & 64 \\ Feedforward network layer dimension & 128 \\ Input data (grid\_size: \(3\times 13\) road network structure) & 13, 3 \\ Batch size & 128 \\ Learning rate (Lr) & 0.001 \\ Optimizer & Adam \\ Training epochs & 200 \\ \(\omega\) & 2 \\ \(\lambda\) & 0.11 \\ \(sw\) & 15 \\ \(\alpha_{c}\) & 35 \\ \(K\) & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter list of the model. multimodal trajectory predictions by effectively modeling dynamic relationships over time and space. Comparative models (4) and (5) serve as ablation experiments for HTSA-LSTM, with A-LSTM using only a unified attention mechanism (AM) and CS-LSTM employing only the spatial attention mechanism (social convolutional network). HTSA-LSTM integrates both, with AM as the temporal attention mechanism and the social convolutional network as the spatial attention mechanism. Models (2) and (3) also contribute to a broader ablation study, where removing the spatiotemporal attention from HTSA-LSTM results in S-LSTM and replacing it with a Maneuver module results in M-LSTM, retaining the overall framework while altering specific components. Table 3 presents the RMSE and NLL values for predictions made by HTSA-LSTM and baseline models on the NGSIM US101 dataset. As shown, the evaluation metrics increase as the prediction horizon extends, indicating a decline in prediction accuracy for all models. Compared to other baseline models, the CV and S-LSTM models produce higher RMSE and NLL values, which can be attributed to the fact that other models consider the influence of interaction information from neighboring vehicles during trajectory prediction. This directly indicates the significance of vehicle interaction information in trajectory prediction.