# 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 intelligent transportation systems, traffic forecasting relies on deep learning models, including graph neural networks (GNNs). This survey provides a comprehensive overview of GNN applications in traffic flow and vehicle speed forecasting [25]. The proposed CTFL framework utilizes a divide-and-conquer approach to address communication overhead in federated learning (FL) with GNN-based traffic forecasting models. By clustering clients based on local model parameters' closeness and employing a two-step strategy for optimizing local models, CTFL showcases outstanding training efficiency and prediction accuracy in case studies on real-world datasets and GNN-based models [26]. Integrating multimodal data introduces challenges in terms of data complexity and volume. Efficiently managing and extracting insights from these data is pivotal for informed decision-making. Machine learning and data fusion techniques are highlighted as indispensable tools for unravelling meaningful patterns and optimizing urban services [27]. The synergy between machine learning and data fusion techniques becomes a cornerstone in the smart city landscape. Predictive machine learning models can forecast traffic congestion, optimize routing, and improve air quality. Energy management systems developed through machine learning contribute to reduced energy consumption and increased reliance on renewable sources [28]. GCNs, a specialized neural network tailored for graph-structured data, hold immense potential for smart cities. GCNs contribute to more accurate predictions and facilitate informed decision-making in dynamic urban environments by capturing spatial relationships and dependencies within urban networks [29]. The smart city concept employs advanced technologies such as graph convolutional networks (GCNs) to optimize urban infrastructure and enhance the quality of life for residents. GCNs are integrating graph convolutional networks into smart city frameworks for enhanced traffic analysis designed to effectively model complex relationships within spatially distributed data, making them particularly adept at traffic forecasting and urban mobility analysis. In a smart city context, GCNs operate on graph-structured data where nodes represent various urban elements such as intersections, bus stops, or parking spaces and edges denote the spatial relationships between these nodes. This graph-based representation allows GCNs to capture intricate spatial dependencies [30], such as the influence of neighboring road segments on traffic flow at a given location. By utilizing adjacency matrices, GCNs can incorporate information from connected nodes, enhancing the model's ability to predict traffic states like flow, speed, and congestion. Graph convolutional networks (GCNs) can be adapted to capture temporal dynamics by incorporating temporal convolutions or recurrent neural network (RNN) components, enabling them to process time-series data and improve accuracy in short-term traffic forecasting. Similarly, accurate traffic forecasting requires effectively capturing both long- and short-term patterns, which standard models often miss. The LSTTN framework addresses this gap by using a Transformer to learn key features from long traffic histories, enhancing predictions through an integration of long-term, periodic, and short-term trends, resulting in up to a 16.78% improvement over baseline models in long-term forecasting [31]. In a different context, LAMP in graph attention networks (GATs) improves node classification by learning the importance of neighboring nodes. However, in heterophilic graphs, dissimilar neighbors can cause a \"Distraction Effect\" (DE) that weakens performance. To mitigate this, CAT selectively removes distracting neighbors, boosting accuracy without altering the GAT architecture [32]. The application of GCNs in smart cities supports the development of adaptive traffic signal control systems that adjust in real time based on current traffic conditions.