# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 2 Related Work
### Advancements in Traffic Prediction Innovations and Solutions Using Graph Convolution Network  
In the realm of smart cities, the intricate dynamics of traffic prediction present formidable challenges arising from spatio-temporal complexities and non-linear dynamics. Conventional methods grapple with high training costs and difficulty accurately capturing patterns, often neglecting correlations between distant roads. This paper addresses these challenges by introducing innovative solutions. One such solution is using 3D temporal graph convolutional networks (3D-TGCNs) for traffic prediction. Constructing road graphs based on time series similarity makes spatial information redundant. The models original 3D graph convolution approach markedly enhances accuracy, surpassing current baselines in empirical results [17]. Similarly, traffic forecasting encounters hurdles in capturing complex spatial-temporal correlations and non-linear patterns. Many existing approaches overlook direct spatial-temporal correlations, modeling these dependencies separately. The adaptive graph spatial-temporal transformer network (ASTTN) emerges as a breakthrough, drawing inspiration from the success of transformers. By directly modeling cross-spatial-temporal correlations using local multi-head self-attentions on an adaptive spatial-temporal graph, ASTTN excels in empirical results [18]. Traffic prediction becomes challenging in real-world applications due to dynamic spatiotemporal dependencies among traffic data. The adaptive spatio-temporal convolutional network (ASTCN) is introduced to tackle these challenges, incorporating a spatial graph learning module and an adaptive temporal convolution module. ASTCN consistently outperforms other models in extensive experiments on real-world traffic datasets [19]. Complex spatial-temporal correlations on road networks further complicate traffic forecasting. The DualGraph framework is introduced to address this limitation by simultaneously modeling traffic forecasting for nodes and edges. Empirical evaluations of public datasets demonstrate the effectiveness of the proposed method, especially in long-term predictions [20]. In fields like neurology and transportation, spatiotemporal forecasting faces complexity due to non-linearity, dynamic nature, and shifting road conditions. The dynamic temporal position observant graph neural network (DTPO-GNN) handles these challenges, incorporating positional awareness and spatial-temporal reliance on traffic flow. Controlled sample encoder-decoder architecture and two-way random walks make DTPO-GNN an efficient solution for accurate traffic forecasting in large-scale road networks [21]. Time series forecasting, crucial for applications like predicting solar plant energy output and traffic situations, is addressed with the transformer model. The study introduces convolutional self-attention and the LogSparse transformer to overcome insensitivity to local context and a memory bottleneck. Experimental results on synthetic and real-world datasets showcase the superiority of the proposed model [22]. Federated learning safeguards data privacy in intelligent transportation systems but often neglects topological information of transportation networks. The attention-based spatial-temporal graph neural networks (ASTGNN) model, integrated with a novel federated learning framework called FASTGNN, addresses this gap. The framework preserves topological information using a differential privacy-based approach, enabling local GNN-based models to access the global network for enhanced training [23]. Predicting traffic speed in intelligent traffic systems (ITSs) is challenging due to complex spatial-temporal correlations. The proposed GSTGCN, a deep-learning model for urban traffic speed prediction, effectively captures various correlations and external influences. Experimental results on real-world traffic datasets demonstrate GSTGCN's superiority over state-of-the-art baselines [24].