Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution

• URL: http://arxiv.org/abs/2104.14917v2
• Date: Mon, 3 May 2021 14:11:25 GMT
• Title: Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
• Authors: Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Depeng Jin, and Yong Li
• Abstract summary: We propose a novel traffic prediction framework, named Dynamic Graph Contemporalal Recurrent Network (DGCRN) In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes. We are the first to employ a generation method to model fine iteration of dynamic graph at each time step.
• Score: 18.309299822858243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic prediction is the cornerstone of an intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road networks. Meanwhile, most Recurrent Neural Network (RNN) based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this paper, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic filters are generated at each time step. We filter the node embeddings and then use them to generate a dynamic graph, which is integrated with a pre-defined static graph. As far as we know, we are the first to employ a generation method to model fine topology of dynamic graph at each time step. Further, to enhance efficiency and performance, we employ a training strategy for DGCRN by restricting the iteration number of decoder during forward and backward propagation. Finally, a reproducible standardized benchmark and a brand new representative traffic dataset are opened for fair comparison and further research. Extensive experiments on three datasets demonstrate that our model outperforms 15 baselines consistently.

Related papers

• Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks [66.70786325911124]
  Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data. With the increasing prevalence of DGNNs, it becomes imperative to investigate the implementation of dynamic graph unlearning. We propose an effective, efficient, model-agnostic, and post-processing method to implement DGNN unlearning.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• Attention-based Dynamic Graph Convolutional Recurrent Neural Network for Traffic Flow Prediction in Highway Transportation [0.6650227510403052]
  Attention-based Dynamic Graph Convolutional Recurrent Neural Network (ADG-N) is proposed to improve traffic flow prediction in highway transportation. A dedicated gated kernel emphasizing highly relative nodes is introduced on complete graphs to reduce overfitting for graph convolution operations.
  arXiv  Detail & Related papers  (2023-09-13T13:57:21Z)
• Dynamic Causal Graph Convolutional Network for Traffic Prediction [19.759695727682935]
  We propose an approach for predicting traffic that embeds time-varying dynamic network to capture finetemporal patterns of traffic data. We then use graph convolutional networks to generate traffic forecasts. Our experimental results on a real traffic dataset demonstrate the superior prediction performance of the proposed method.
  arXiv  Detail & Related papers  (2023-06-12T10:46:31Z)
• Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting [60.03169701753824]
  We propose a novel Dynamic Diffusion-al Graph Neural Network (DVGNN) fortemporal forecasting. The proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result.
  arXiv  Detail & Related papers  (2023-05-16T11:38:19Z)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• A Dynamic Temporal Self-attention Graph Convolutional Network for Traffic Prediction [7.23135508361981]
  This paper proposes a temporal self-attention graph convolutional network (DT-SGN) model which considers the adjacent matrix as a trainable attention score matrix. Experiments demonstrate the superiority of our method over state-of-art model-driven model and data-driven models on real-world traffic datasets.
  arXiv  Detail & Related papers  (2023-02-21T03:51:52Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction [6.968068088508505]
  We present a new sequence-to-sequence architecture for graph neural networks (GNNs) We also present a new benchmark benchmark dataset of street-level segment data in Montreal, Canada. Our model improves performance by more than 7% for one-hour prediction compared to the baseline methods.
  arXiv  Detail & Related papers  (2022-09-08T14:56:29Z)
• Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting [1.52292571922932]
  We propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) for traffic forecasting in this paper. In STIDGCN, we propose an interactive dynamic graph convolution structure, which first divides the sequences at intervals and captures the spatial-temporal dependence of the traffic data simultaneously. Experiments on four real-world traffic flow datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline.
  arXiv  Detail & Related papers  (2022-05-18T01:59:30Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting [47.19400232038575]
  We argue that learning node-specific patterns is essential for traffic forecasting while the pre-defined graph is avoidable. We propose two adaptive modules for enhancing Graph Conal Network (GCN) with new capabilities. Our experiments on two real-world traffic datasets show AGCRN outperforms state-of-the-art by a significant margin without pre-defined graphs about spatial connections.
  arXiv  Detail & Related papers  (2020-07-06T15:51:10Z)

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.