Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition

• URL: http://arxiv.org/abs/2212.05653v1
• Date: Mon, 12 Dec 2022 01:44:52 GMT
• Title: Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
• Authors: Qin Li, Xuan Yang, Yong Wang, Yuankai Wu, Deqiang He
• Abstract summary: Graph convolutional networks (GCNs) have been widely used in traffic flow prediction. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition.
• Score: 10.104097475236014
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers to take control measures and drivers to choose the optimal travel routes. Recently, graph convolutional networks (GCNs) have been widely used in traffic flow prediction owing to their powerful ability to capture spatial-temporal dependencies. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs, and it is still an open question. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed. First, we reformulate the spatial-temporal fusion graph adjacency matrix into a three-way adjacency tensor. Then, we reconstructed the adjacency tensor via Tucker decomposition, wherein more informative and global spatial-temporal dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph Convolutional module for localized spatial-temporal correlations learning and a Dilated Convolution module for global correlations learning are assembled to aggregate and learn the comprehensive spatial-temporal dependencies of the road network. Experimental results on four open-access datasets demonstrate that the proposed model outperforms state-of-the-art approaches in terms of the prediction performance and computational cost.

Related papers

• Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework [2.9490249935740573]
  We propose a Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework (FMPESTF) FMPESTF is composed of spatial and temporal modules for down-sampling traffic data. We introduce attention mechanism in time modeling, and design hierarchical spatial-temporal interactive learning to help the model adapt to various traffic scenarios.
  arXiv  Detail & Related papers  (2024-10-12T03:47:27Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting [12.568905377581647]
  Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. Existing methods cannot accurately model both long-term and short-term temporal correlations simultaneously. We propose a novel spatial-temporal neural network framework, which consists of a graph convolutional recurrent module (GCRN) and a global attention module.
  arXiv  Detail & Related papers  (2023-02-25T03:37:00Z)
• Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction [10.3426659705376]
  We propose a novel dynamic graph convolution network with attention fusion to model synchronous spatial-temporal correlations. We conduct extensive experiments in four real-world traffic datasets to demonstrate that our method surpasses state-of-the-art performance compared to 18 baseline methods.
  arXiv  Detail & Related papers  (2023-02-24T12:21:30Z)
• 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)
• STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting [7.232141271583618]
  We propose a novel deep learning model for traffic forecasting named inefficient-Context Spatio-Temporal Joint Linear Attention (SSTLA) SSTLA applies linear attention to a joint graph to capture global dependence between alltemporal- nodes efficiently. Experiments on two real-world traffic datasets, England and Temporal7, demonstrate that our STJLA can achieve 9.83% and 3.08% 3.08% accuracy in MAE measure over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-12-04T06:39:18Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)
• 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)
• Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [35.072979313851235]
  spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks typically utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. This paper proposes Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting.
  arXiv  Detail & Related papers  (2020-12-15T14:03:17Z)
• 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)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.