593 research outputs found
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph structure (relation) does not necessarily reflect the true dependency and genuine relation may be missing due to the incomplete connections in the data. Furthermore, existing methods are ineffective to capture the temporal trends as the RNNs or CNNs employed in these methods cannot capture long-range temporal sequences. To overcome these limitations, we propose in this paper a novel graph neural network architecture, Graph WaveNet, for spatial-temporal graph modeling. By developing a novel adaptive dependency matrix and learn it through node embedding, our model can precisely capture the hidden spatial dependency in the data. With a stacked dilated 1D convolution component whose receptive field grows exponentially as the number of layers increases, Graph WaveNet is able to handle very long sequences. These two components are integrated seamlessly in a unified framework and the whole framework is learned in an end-to-end manner. Experimental results on two public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm
GMAN: A Graph Multi-Attention Network for Traffic Prediction
Long-term traffic prediction is highly challenging due to the complexity of
traffic systems and the constantly changing nature of many impacting factors.
In this paper, we focus on the spatio-temporal factors, and propose a graph
multi-attention network (GMAN) to predict traffic conditions for time steps
ahead at different locations on a road network graph. GMAN adapts an
encoder-decoder architecture, where both the encoder and the decoder consist of
multiple spatio-temporal attention blocks to model the impact of the
spatio-temporal factors on traffic conditions. The encoder encodes the input
traffic features and the decoder predicts the output sequence. Between the
encoder and the decoder, a transform attention layer is applied to convert the
encoded traffic features to generate the sequence representations of future
time steps as the input of the decoder. The transform attention mechanism
models the direct relationships between historical and future time steps that
helps to alleviate the error propagation problem among prediction time steps.
Experimental results on two real-world traffic prediction tasks (i.e., traffic
volume prediction and traffic speed prediction) demonstrate the superiority of
GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms
state-of-the-art methods by up to 4% improvement in MAE measure. The source
code is available at https://github.com/zhengchuanpan/GMAN.Comment: AAAI 2020 pape
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting
Forecasting of multivariate time-series is an important problem that has
applications in traffic management, cellular network configuration, and
quantitative finance. A special case of the problem arises when there is a
graph available that captures the relationships between the time-series. In
this paper we propose a novel learning architecture that achieves performance
competitive with or better than the best existing algorithms, without requiring
knowledge of the graph. The key element of our proposed architecture is the
learnable fully connected hard graph gating mechanism that enables the use of
the state-of-the-art and highly computationally efficient fully connected
time-series forecasting architecture in traffic forecasting applications.
Experimental results for two public traffic network datasets illustrate the
value of our approach, and ablation studies confirm the importance of each
element of the architecture. The code is available here:
https://github.com/boreshkinai/fc-gaga
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
Traffic forecasting is of great importance to transportation management and
public safety, and very challenging due to the complicated spatial-temporal
dependency and essential uncertainty brought about by the road network and
traffic conditions. Latest studies mainly focus on modeling the spatial
dependency by utilizing graph convolutional networks (GCNs) throughout a fixed
weighted graph. However, edges, i.e., the correlations between pair-wise nodes,
are much more complicated and interact with each other. In this paper, we
propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep
learning model for traffic forecasting. We first build the node-wise graph
according to the road network distance and the edge-wise graph according to
various edge interaction patterns. Then, we implement the interactions of both
nodes and edges using bicomponent graph convolution. The multi-range attention
mechanism is introduced to aggregate information in different neighborhood
ranges and automatically learn the importance of different ranges. Extensive
experiments on two real-world road network traffic datasets, METR-LA and
PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.Comment: Accepted by AAAI 202
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