STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting

• URL: http://arxiv.org/abs/2410.00385v2
• Date: Tue, 15 Oct 2024 05:44:29 GMT
• Title: STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
• Authors: Hongjun Wang, Jiyuan Chen, Tong Pan, Zheng Dong, Lingyu Zhang, Renhe Jiang, Xuan Song,
• Abstract summary: Traffic is a cornerstone of smart city management enabling efficient allocation and transportation planning. Deep learning, with its ability to capture complex nonlinear patterns in data, has emerged as a powerful tool for traffic forecasting. graph neural networks (GCNs) and transformer-based models have shown promise, but their computational demands often hinder their application to realworld networks. We propose a noveltemporal graph transformer (STG) architecture, enabling efficient modeling of both global and local traffic patterns while maintaining a manageable computational footprint.
• Score: 11.208740750755025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture complex nonlinear patterns in spatiotemporal (ST) data, has emerged as a powerful tool for traffic forecasting. While graph neural networks (GCNs) and transformer-based models have shown promise, their computational demands often hinder their application to real-world road networks, particularly those with large-scale spatiotemporal interactions. To address these challenges, we propose a novel spatiotemporal graph transformer (STGformer) architecture. STGformer effectively balances the strengths of GCNs and Transformers, enabling efficient modeling of both global and local traffic patterns while maintaining a manageable computational footprint. Unlike traditional approaches that require multiple attention layers, STG attention block captures high-order spatiotemporal interactions in a single layer, significantly reducing computational cost. In particular, STGformer achieves a 100x speedup and a 99.8\% reduction in GPU memory usage compared to STAEformer during batch inference on a California road graph with 8,600 sensors. We evaluate STGformer on the LargeST benchmark and demonstrate its superiority over state-of-the-art Transformer-based methods such as PDFormer and STAEformer, which underline STGformer's potential to revolutionize traffic forecasting by overcoming the computational and memory limitations of existing approaches, making it a promising foundation for future spatiotemporal modeling tasks.

Related papers

• Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting [9.818230821091184]
  Rapid urbanization has led to dynamic shifts in traffic patterns and travel demand, posing major challenges for accurate long-term traffic prediction. We evaluate state-of-the-art models on an extended traffic benchmark and observe substantial performance degradation in existing ST-GNNs. We propose a Principal Component Analysis (PCA) embedding approach that enables models to adapt to new scenarios without retraining.
  arXiv  Detail & Related papers  (2024-11-18T10:30:34Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• DST-TransitNet: A Dynamic Spatio-Temporal Deep Learning Model for Scalable and Efficient Network-Wide Prediction of Station-Level Transit Ridership [12.6020349733674]
  This paper introduces DST-TransitNet, a hybrid Deep Learning model for system-wide ridership prediction. It is tested on Bogota's BRT system data, with three distinct social scenarios. It outperforms state-of-the-art models in precision, efficiency and robustness.
  arXiv  Detail & Related papers  (2024-10-19T06:59:39Z)
• Spatio-Temporal Meta Contrastive Learning [18.289397543341707]
  We propose a new-temporal contrastive learning framework to encode robust and generalizable S-temporal Graph representations. We show that our framework significantly improves performance over various state-of-the-art baselines in traffic crime prediction.
  arXiv  Detail & Related papers  (2023-10-26T04:56:31Z)
• ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting [47.74479442786052]
  Current research on Spatio-Temporal Graph Neural Networks (STGNNs) often prioritizes complex designs, leading to computational burdens with only minor enhancements in accuracy. We propose ST-MLP, a concise cascaded temporal-temporal model solely based on Multi-Layer Perceptron (MLP) modules and linear layers. Empirical results demonstrate that ST-MLP outperforms state-of-the-art STGNNs and other models in terms of accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2023-08-14T23:34:59Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction [0.40964539027092917]
  We propose STLGRU, a novel traffic forecasting model for predicting traffic flow accurately. Our proposed STLGRU can effectively capture dynamic local and global spatial-temporal relations of traffic networks. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2022-12-08T20:24:59Z)
• Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks [110.80088437391379]
  A graph-based framework called SMART is proposed to model and keep track of the statistics of vehicle-to-temporal (V2I) communication latency across a large geographical area. We develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and the latency performance of large vehicular networks.
  arXiv  Detail & Related papers  (2021-03-13T06:56:29Z)
• Spatio-Temporal Graph Scattering Transform [54.52797775999124]
  Graph neural networks may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. We put forth a novel mathematically designed framework to analyze-temporal data.
  arXiv  Detail & Related papers  (2020-12-06T19:49:55Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.