Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
- URL: http://arxiv.org/abs/2007.02842v2
- Date: Thu, 22 Oct 2020 00:08:46 GMT
- Title: Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
- Authors: Lei Bai and Lina Yao and Can Li and Xianzhi Wang and Can Wang
- Abstract summary: 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.
- Score: 47.19400232038575
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modeling complex spatial and temporal correlations in the correlated time
series data is indispensable for understanding the traffic dynamics and
predicting the future status of an evolving traffic system. Recent works focus
on designing complicated graph neural network architectures to capture shared
patterns with the help of pre-defined graphs. In this paper, we argue that
learning node-specific patterns is essential for traffic forecasting while the
pre-defined graph is avoidable. To this end, we propose two adaptive modules
for enhancing Graph Convolutional Network (GCN) with new capabilities: 1) a
Node Adaptive Parameter Learning (NAPL) module to capture node-specific
patterns; 2) a Data Adaptive Graph Generation (DAGG) module to infer the
inter-dependencies among different traffic series automatically. We further
propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture
fine-grained spatial and temporal correlations in traffic series automatically
based on the two modules and recurrent networks. 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.
Related papers
- 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 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) - Spatial-Temporal Adaptive Graph Convolution with Attention Network for
Traffic Forecasting [4.1700160312787125]
We propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting.
Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes.
Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block.
arXiv Detail & Related papers (2022-06-07T09:08:35Z) - 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) - PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting [4.14360329494344]
We propose a novel traffic forecasting framework called Progressive Graph Convolutional Network (PGCN)
PGCN constructs a set of graphs by progressively adapting to online input data during the training and testing phases.
The proposed model achieves state-of-the-art performance with consistency in all datasets.
arXiv Detail & Related papers (2022-02-18T02:15:44Z) - 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) - Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text
Generation [56.73834525802723]
Lightweight Dynamic Graph Convolutional Networks (LDGCNs) are proposed.
LDGCNs capture richer non-local interactions by synthesizing higher order information from the input graphs.
We develop two novel parameter saving strategies based on the group graph convolutions and weight tied convolutions to reduce memory usage and model complexity.
arXiv Detail & Related papers (2020-10-09T06:03:46Z) - Understanding Dynamic Scenes using Graph Convolution Networks [22.022759283770377]
We present a novel framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed by a moving camera.
We show a seamless transfer of learning to multiple datasets without resorting to fine-tuning.
Such behavior prediction methods find immediate relevance in a variety of navigation tasks.
arXiv Detail & Related papers (2020-05-09T13:05:06Z) - 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.