###### Abstract  
Trajectory prediction is a key component in the development of applications such as mixed urban traffic management and public safety. Traditional models have struggled with the complexity of modeling dynamic crowd interactions, the intricacies of spatiotemporal dependencies, and environmental constraints. Addressing these challenges, this paper introduces the innovative Social Attention Graph Neural Network (SA-GAT) framework. Utilizing Long Short-Term Memory (LSTM) networks, SA-GAT encodes pedestrian trajectory data to extract temporal correlations, while Graph Attention Networks (GAT) are employed to precisely capture the subtle interactions among pedestrians. The SA-GAT framework boosts its predictive accuracy with two key innovations. First, it features a Scene Potential Module that utilizes a Scene Tensor to dynamically capture the interplay between crowds and their environment. Second, it incorporates a Transition Intention Module with a Transition Tensor, which interprets latent transfer probabilities from trajectory data to reveal pedestrians' implicit intentions at specific locations. Based on AnyLogic modeling of the metro station on Line 10 of Chengdu Shuangliu Airport, China, numerical studies reveal that the SA-GAT model achieves a substantial reduction in ADE and FDE metrics by 34.22% and 38.04% compared to baseline models.  
graph attention network; social and scene interactions; passenger behavior +
Footnote †: journal: Applied  
0000-0001-5002-499X/00/0000-0001-5002-499X/000  
inaccuracies. Alternatively, pedestrian intentions are defined as potential goals or destinations [15; 16] to refine trajectory predictions. By considering intentions as dynamic and integrating them into our trajectory prediction model, we capture the multifaceted nature of the pedestrian movement, recognizing the significance of multiple potential destinations within the urban landscape.  
To fill this gap, this research proposes a novel SA-GAT (Social Attention Graph Neural Network) framework for Pedestrian Trajectory Prediction (PTP) tasks.  
The major contributions of this study can be succinctly described as follows.  
1. The Social Attention Graph Neural Network (SA-GAT).
2. We propose a new trajectory prediction model that uses LSTM networks to encode pedestrian trajectories and extract temporal correlations, while Graph Attention Networks (GAT) are employed to precisely capture the subtle interactions among pedestrians. The SA-GAT model achieves exceptional performance on the validation dataset by analyzing the various factors that affect pedestrian trajectory prediction in real-world situations.
3. A Scene Potential Module that utilizes a Scene Tensor to dynamically capture the interaction between a crowd and its environment. The tensor is constructed from a large dataset of historical spatiotemporal pedestrian trajectories.
4. A Transition Intention Module that utilizes a Transition Tensor, interpreting potential transfer probabilities in trajectory data to reveal the implicit intent of pedestrians at specific locations.
5. Realistic Pedestrian Data via AnyLogic Simulation: Utilizing a model of Chengdu metro station, we generated detailed spatiotemporal trajectory data for approximately 3500 pedestrians over a duration of 120 min, providing a solid empirical basis for our study.  
In this section, we outline the background of the study and provide an overview of the article's structure as follows: Section 2 reviews existing studies on pedestrian trajectory prediction; Section 3 presents our proposed methodology and the criteria for evaluating the model's quality; Section 4 displays the experimental results and provides an analysis; finally, Section 5 summarizes our study and offers an outlook for future research.