Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction

Li, Fuxian; Yan, Huan; Jin, Guangyin; Liu, Yue; Li, Yong; Jin, Depeng

doi:10.1145/3511808.3557243

Cited by 15 publications

(3 citation statements)

References 14 publications

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“…Convolutional Neural Networks (CNNs) were then applied to grid-based traffic data to capture spatial dependencies (Zhang, Zheng, and Qi 2017;Lin et al 2020). Graph Neural Networks (GNNs) gained prominence for traffic prediction, leveraging their ability to model graph data (James 2022;Yu, Yin, and Zhu 2018;Song et al 2020;Wu et al 2020;Shao et al 2022b;Li et al 2022;Shang, Chen, and Bi 2021;Choi et al 2022;Jiang et al 2023b). The attention mechanism gained popularity for dynamic dependency modeling (Guo et al 2019;Zheng et al 2020).…”

Section: Related Workmentioning

confidence: 99%

ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

Sun,

Liu,

et al. 2024

AAAI

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Traffic prediction is the core issue of Intelligent Transportation Systems. Recently, researchers have tended to use complex structures, such as transformer-based structures, for tasks such as traffic prediction. Notably, traffic data is simpler to process compared to text and images, which raises questions about the necessity of these structures. Additionally, when handling traffic data, researchers tend to manually design the model structure based on the data features, which makes the structure of traffic prediction redundant and the model generalizability limited. To address the above, we introduce the ‘ModWaveMLP’—A multilayer perceptron (MLP) based model designed according to mode decomposition and wavelet noise reduction information learning concepts. The model is based on simple MLP structure, which achieves the separation and prediction of different traffic modes and does not depend on additional features introduced such as the topology of the traffic network. By performing experiments on real-world datasets METR-LA and PEMS-BAY, our model achieves SOTA, outperforms GNN and transformer-based models, and outperforms those that introduce additional feature data with better generalizability, and we further demonstrate the effectiveness of the various parts of the model through ablation experiments. This offers new insights to subsequent researchers involved in traffic model design. The code is available at: https://github.com/Kqingzheng/ModWaveMLP.

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Section: Related Workmentioning

confidence: 99%

ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

Sun,

Liu,

et al. 2024

AAAI

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show abstract

“…Temporal Component [9] GNN GRU [10] GNN GRU [11] GNN GRU [12] GNN GRU [13] GNN GRU [14] GNN GRU [15] GNN CNN [16] GNN CNN [17] GNN CNN [18] GNN TCN [19] GNN CNN [20] GNN TCN [21] GNN GRU [22] GNN TCN [23] GNN CNN [24] GNN TCN Moreover, several studies have utilized embedding structures to discover both temporal and spatial features with the goal of efficiently capturing spatial-temporal dependencies. Adaptive Graph Convolutional Recurrent Networks (AGCRNs) [9] are known to constitute a pioneering approach that designs embeddings for each node, thus creating an adaptive graph as opposed to a predefined one, establishing spatial connections and capturing spatial correlations.…”

Section: Study Spatial Componentmentioning

confidence: 99%

“…Initially, several studies employed convolutional neural networks (CNNs) to analyze traffic data based on grid structures to capture spatial correlations [5,6]. Subsequently, graph neural networks (GNNs) demonstrated their suitability to model underlying graph structures [7,8]; thus, the methods based on GNNs have been extensively investigated for traffic flow prediction [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Considering the handling of temporal correlations, these GNN models primarily fall into two categories: RNN-based and CNN-based models.…”

Section: Introductionmentioning

confidence: 99%

STFEformer: Spatial–Temporal Fusion Embedding Transformer for Traffic Flow Prediction

Yang,

Wei,

Wang

2024

Applied Sciences

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In the realm of Intelligent Transportation Systems (ITSs), traffic flow prediction is crucial for multiple applications. The primary challenge in traffic flow prediction lies in the handling and modeling of the intricate spatial–temporal correlations inherent in transport data. In recent years, many studies have focused on developing various Spatial–Temporal Graph Neural Networks (STGNNs), and researchers have also begun to explore the application of transformers to capture spatial–temporal correlations in traffic data. However, GNN-based methods mainly focus on modeling spatial correlations statically, which significantly limits their capacity to discover dynamic and long-range spatial patterns. Transformer-based methods have not sufficiently extracted the comprehensive representation of traffic data features. To explore dynamic spatial dependencies and comprehensively characterize traffic data, the Spatial–Temporal Fusion Embedding Transformer (STFEformer) is proposed for traffic flow prediction. Specifically, we propose a fusion embedding layer to capture and fuse both native information and spatial–temporal features, aiming to achieve a comprehensive representation of traffic data characteristics. Then, we introduce a spatial self-attention module designed to enhance detection of dynamic and long-range spatial correlations by focusing on interactions between similar nodes. Extensive experiments conducted on three real-world datasets demonstrate that STFEformer significantly outperforms various baseline models, notably achieving up to a 5.6% reduction in Mean Absolute Error (MAE) on the PeMS08 dataset compared to the next-best model. Furthermore, the results of ablation experiments and visualizations are employed to clarify and highlight our model’s performance. STFEformer represents a meaningful advancement in traffic flow prediction, potentially influencing future research and applications in ITSs by providing a more robust framework for managing and analyzing traffic data.

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MGCP: A Multi-View Diffusing Graphs Based Traffic Congestion Prediction for Roads Around Factory

Zhao,

Shen,

Mao

et al. 2023

Lecture Notes in Computer Science

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Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction

Cited by 15 publications

References 14 publications

ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

STFEformer: Spatial–Temporal Fusion Embedding Transformer for Traffic Flow Prediction

MGCP: A Multi-View Diffusing Graphs Based Traffic Congestion Prediction for Roads Around Factory

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