Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.

In recent years, collaborative classification of multimodal data, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR), has been widely used to improve remote sensing image classification accuracy. However, existing fusion approaches for HSI and LiDAR suffer from limitations. Fusing the heterogeneous features of HSI and LiDAR proved to be challenging, leading to incomplete utilization of information for category representation. Additionally, during the extraction of spatial features from HSI, the spectral and spatial information are often disjointed. It leads to the difficulty of fully exploiting the rich spectral information in hyperspectral data. To address these issues, we proposed a multimodal data fusion framework specifically designed for HSI and LiDAR fusion classification, called Modality Fusion Vision Transformer (MFViT). We have designed a stackable modality fusion block (MFB) as the core of our model. Specifically, these blocks mainly consist of multimodal cross-attention modules and spectral self-attention modules. The proposed novel multimodal cross-attention module for feature fusion addresses the issue of insufficient fusion of heterogeneous features from HSI and LiDAR for category representation. Compared to other cross-attention methods, it reduces the alignment requirements between modal feature spaces during cross-modal fusion. The spectral self-attention module can preserve spatial features while exploiting the rich spectral information and participating in the process of extracting spatial features from HSI. Ultimately, we achieve overall classification accuracies of 99.91%, 99.59%, and 96.98% on three benchmark datasets respectively, surpassing all state-of-the-art methods, demonstrating the stability and effectiveness of our model.