## 4 Experiments
### Quantitative Evaluation on the Model  
#### 4.2.1 Quantitative Evaluation of AnyLogic Simulation Datasets  
The quantitative assessment uses ADE and FDE measures to compare against baseline models. As in most previous studies, we set the predicted time step to 12 timeframes. Detailed in Table 1, the results show that our proposed model outperforms the competing models in both measures. Furthermore, to further validate the responsiveness of our model to variations in the prediction time step, we systematically adjusted it to 8, 16, and 20 timeframes, respectively. The experimental findings from Table 2 demonstrate that our model achieves the best performance when the time step is set to 8. As the prediction step length increases, model accuracy declines, likely due to error accumulation causing deviations from the actual trajectory. Additionally, predicting long time steps involves forecasting trajectories over extended periods, where increased uncertainty in pedestrian behavior makes accurate prediction more challenging. The movement trajectories of pedestrians in our dataset are more complex and variable than those in the publicly available dataset, so the prediction will be more difficult. Different scenarios (e.g., indoor, outdoor, crowdedness) will affect the movement patterns of pedestrians and the difficulty of prediction. Therefore, it leads to high ADE and FDE metrics. Table 1 reveals that, aside from our model, STGAT emerges as the most effective on the simulated dataset. STGAT accounts for pedestrian interactions and captures both temporal and spatial dynamics of pedestrian movements. Consequently, its ADE and FDE metrics are notably lower than those of the Social-Attention model by 13.90% and 12.56%, respectively. In contrast, the LSTM model yields predictions with significantly higher errors, a trend largely attributable to the crowded scenarios prevalent in our dataset. This observation underscores the importance of incorporating pedestrian interactions in crowded settings, a feature the LSTM model lacks, leading to suboptimal prediction outcomes. Conversely, the Social-LSTM model combines the hidden states of pedestrians together with those of their nearby neighbors. This enhancement substantially bolsters the model's proficiency in capturing pedestrian interactions, leading to a remarkable reduction in ADE and FDE metrics of 34.64% and 32.53%, with respect to the conventional LSTM model. This improvement highlights the critical role of social pooling in enhancing predictive accuracy within crowded environments. Models that account for pedestrian interactions, including Social-Attention, STGAT, and Social-STGCNN, demonstrate substantial improvements in prediction accuracy. Notably, the Social-Attention model leverages a pedestrian spatiotemporal graph to depict interactions among pedestrians, enabling effective extraction of interaction features. As a result, the ADE and FDE metrics of the Social-Attention model were significantly reduced by 22.13% and 16.91%, compared to the Social-LSTM model. This advancement underscores the effectiveness of the graph-based model to enhance the understanding and prediction of pedestrian dynamics. The Social-STGCNN model utilizes a spatiotemporal graph convolutional layer to efficiently capture features in the pedestrian trajectory data, demonstrating robust performance across public datasets. However, its efficacy is somewhat diminished in our indoor settings, characterized by dense pedestrian traffic. This performance dip may partly arise from the model's inadequate handling of environmental obstacles. Additionally, the Social-GAN model, leveraging generative adversarial networks, shows promise on public datasets but reveals limitations on our specialized dataset. These limitations suggest that