Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep
Learning Approach Considering Dynamic Non-Local Spatial Correlation and
Non-Stationary Temporal Dependency
- URL: http://arxiv.org/abs/2004.02391v1
- Date: Mon, 6 Apr 2020 03:40:56 GMT
- Title: Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep
Learning Approach Considering Dynamic Non-Local Spatial Correlation and
Non-Stationary Temporal Dependency
- Authors: Xinglei Wang, Xuefeng Guan, Jun Cao, Na Zhang, Huayi Wu
- Abstract summary: This research studies two particular problems in traffic forecasting: (1) capture the dynamic and non-local spatial correlation between traffic links and (2) model the dynamics of temporal dependency for accurate multiple steps ahead predictions.
We propose a deep learning framework named Spatial-Temporal Sequence to Sequence model (STSeq2Seq) to address these issues.
This model builds on sequence to sequence (seq2seq) architecture to capture temporal feature and relies on graph convolution for aggregating spatial information.
- Score: 6.019104024723682
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Obtaining accurate information about future traffic flows of all links in a
traffic network is of great importance for traffic management and control
applications. This research studies two particular problems in traffic
forecasting: (1) capture the dynamic and non-local spatial correlation between
traffic links and (2) model the dynamics of temporal dependency for accurate
multiple steps ahead predictions. To address these issues, we propose a deep
learning framework named Spatial-Temporal Sequence to Sequence model
(STSeq2Seq). This model builds on sequence to sequence (seq2seq) architecture
to capture temporal feature and relies on graph convolution for aggregating
spatial information. Moreover, STSeq2Seq defines and constructs pattern-aware
adjacency matrices (PAMs) based on pair-wise similarity of the recent traffic
patterns on traffic links and integrate it into graph convolution operation. It
also deploys a novel seq2sesq architecture which couples a convolutional
encoder and a recurrent decoder with attention mechanism for dynamic modeling
of long-range dependence between different time steps. We conduct extensive
experiments using two publicly-available large-scale traffic datasets and
compare STSeq2Seq with other baseline models. The numerical results demonstrate
that the proposed model achieves state-of-the-art forecasting performance in
terms of various error measures. The ablation study verifies the effectiveness
of PAMs in capturing dynamic non-local spatial correlation and the superiority
of proposed seq2seq architecture in modeling non-stationary temporal dependency
for multiple steps ahead prediction. Furthermore, qualitative analysis is
conducted on PAMs as well as the attention weights for model interpretation.
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