## 2 Related Work
### Deep Learning-Based Prediction  
On the other hand, data-driven methods, exemplified by the Social-LSTM [24] and Social-GAN [25], have marked a significant leap forward in understanding complex spatiotemporal dynamics, particularly in mobility research [26]. These methods excel in processing extensive datasets and introduce social pooling to accurately model pedestrian interactions. Building upon this, [PERSON] et al. [27] identified drawbacks in the simple aggregation of interactions between pedestrians. As a solution, they proposed occupancy maps to extract features of pedestrians' surrounding neighbors. However, the reliance on localized interactions, as critiqued by [PERSON] et al., overlooks the broader influence of distant movements on pedestrian behavior, revealing a gap in capturing the global spectrum of pedestrian dynamics. The influence of nearby pedestrians moving in the same direction is often minimal on an individual's future path. In contrast, those at a distance can have a considerable impact, as individuals tend to choose paths that avoid potential collisions. This insight challenges the conventional reliance on proximal interactions and underscores the need for models that appreciate the broader context of pedestrian movement. As an alternative, attention mechanisms [28; 29; 30] are introduced to distinguish the importance of different neighbors. However, these methods risk overemphasizing local information, which may potentially ignore the global context. Furthermore, recent approaches have increasingly adopted graph models to harness the temporal evolution of graph topological relationships, which prove to be highly effective in several tasks across various fields, specifically in pedestrian trajectory prediction, where nodes represent interactions between pedestrians [31]. Notably, [PERSON] et al. [32] and [PERSON] et al. [33] utilized Graph Attention Networks to distinguish the impact of individual interactions. However, these studies do not account for the intricate relationship between pedestrians and nearest obstacles. Acknowledging the significance of interactions between pedestrians, recent studies have also underscored the equally crucial impact of interactions with the physical environment on pedestrians' subsequent activities [34; 35]. The adoption of the Convolutional Neural Network (CNN) has marked a leap forward in visual recognition accuracy [28; 36; 37]. By incorporating the analysis of physical environment interactions into the scene context [38], a pooling mechanism is utilized to merge environmental information with a semantic pooling layer, effectively capturing the dynamic interactions between pedestrians and their surroundings. Moreover, scene segmentation [39] distinguishes between roads and obstacles. Social attention and spatial topology models [35] are applied to trajectory prediction in image recognition and behavior analysis. [PERSON] et al. [40] used LSTM-based spatiotemporal maps to capture the interaction between physical obstacles and pedestrians in a given environment. However, these approaches often necessitate extensive pre-training [41], resulting in an overall inflated model size and a substantial consumption of computational complexity, as well as potential performance discrepancies due to semantic deviations [42]. Research on pedestrian trajectory prediction within buildings is also of great significance, with various researchers employing different methods to enhance prediction accuracy. [PERSON] et al. [43] investigated the influence of destination concepts on pedestrian behavior by classifying potential destinations within buildings and predicting future trajectories using a destination-specific trajectory model (DTM). In subsequent work, they highlighted that interactions between pedestrians and facilities also impact future trajectories, leading them to classify pedestrian-facility contact before trajectory prediction [44]. In their latest study, [PERSON] et al.