# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 2 Related Work  
### Research on Spatiotemporal Interactions  
The core challenge of pedestrian trajectory prediction lies in effectively modeling the interactions across spatiotemporal dimensions. In dynamic spatiotemporal interaction modeling, the design of interaction mechanisms is critical for capturing the complex behavioral patterns.  
In the early research, [PERSON] et al. [18] modeled the non-parametric temporal dependencies of pedestrian trajectories using Gaussian process regression and explicitly expressed the uncertainty of movement through Bayesian inference. However, this method struggled to handle multimodal characteristics at path bifurcations and did not fully consider the synergistic effects of multi-scale temporal features in complex scenarios. To address this issue, [PERSON] et al. [19] proposed a Gated Convolutional Neural Network (Gated CNN) that integrates recent and long-term temporal segments. By separating the extraction of inertial features during commuting peaks and real-time fluctuation patterns, and constructing dynamic/static multi-graph structures (such as OD graphs and DTW graphs), they coupled multidimensional spatial associations between locations.  
In the field of mobility behavior recommendation, [PERSON] et al. [20] extracted users' daily behavioral patterns using probabilistic topic models (LDA) and identified shared mobility opportunities in short-distance commutes. Through the mining and matching of spatiotemporal patterns, they leveraged mobility data to support ride-sharing recommendations. [PERSON] et al. [21] analyzed the temporal distribution characteristics of users visiting POIs using DBSCAN clustering, revealing the time-sensitive spatial interaction patterns. By integrating a multi-agent system to dynamically update the user behavior data, they verified that the synergy between temporal preferences and spatial locations significantly improves the recommendation performance. In the studies on spatiotemporal demand prediction and vehicle scheduling for ride-hailing platforms, [PERSON] et al. [22] proposed a prediction model based on random forest regression and a perception-based regional dispatching strategy by integrating multi-dimensional POI spatiotemporal features with meteorological data.  
To address the insufficient integration of individual preferences and dynamic traffic conditions in spatiotemporal interaction modeling, [PERSON] et al. [23] proposed a Multi-Task Spatiotemporal Attention Network (MT-STAN). Through dynamic spatiotemporal attention modules (ST-Blocks), they extracted historical road network associations and utilized Bridge Transformer (Bridge Trans) to transfer future spatiotemporal dependencies. Additionally, they employed cross-network attention mechanisms to uncover the temporal interactions between the individual travel characteristics and traffic conditions.  
[PERSON] et al. [11] proposed a spatiotemporal interaction modeling method (STGAT) based on Graph Attention Networks (GATs) and Long Short-Term Memory (LSTM) networks. By capturing spatial interactions through graph attention mechanisms and explicitly modeling the continuity of temporal interactions with an additional LSTM, this method was the first to combine GATs and LSTM networks to model the spatial relationships and temporal associations between pedestrians. Through a multimodal loss function, it generated diverse socially acceptable trajectories, significantly improving the accuracy of trajectory prediction. In this study, we also investigate the problem of pedestrian trajectory prediction based on spatiotemporal interactions.