DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic
Signal Control
- URL: http://arxiv.org/abs/2109.05491v1
- Date: Sun, 12 Sep 2021 11:27:27 GMT
- Title: DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic
Signal Control
- Authors: Libing Wu, Min Wang, Dan Wu, Jia Wu
- Abstract summary: Adaptive traffic signal control plays a significant role in the construction of smart cities.
We propose a novel neural network framework named DynSTGAT, which integrates dynamic historical state into a new spatial-temporal graph attention network.
Our method can achieve superior performance in travel time and throughput against the state-of-the-art methods.
- Score: 19.0913165219654
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Adaptive traffic signal control plays a significant role in the construction
of smart cities. This task is challenging because of many essential factors,
such as cooperation among neighboring intersections and dynamic traffic
scenarios. First, to facilitate cooperation of traffic signals, existing work
adopts graph neural networks to incorporate the temporal and spatial influences
of the surrounding intersections into the target intersection, where
spatial-temporal information is used separately. However, one drawback of these
methods is that the spatial-temporal correlations are not adequately exploited
to obtain a better control scheme. Second, in a dynamic traffic environment,
the historical state of the intersection is also critical for predicting future
signal switching. Previous work mainly solves this problem using the current
intersection's state, neglecting the fact that traffic flow is continuously
changing both spatially and temporally and does not handle the historical
state.
In this paper, we propose a novel neural network framework named DynSTGAT,
which integrates dynamic historical state into a new spatial-temporal graph
attention network to address the above two problems. More specifically, our
DynSTGAT model employs a novel multi-head graph attention mechanism, which aims
to adequately exploit the joint relations of spatial-temporal information.
Then, to efficiently utilize the historical state information of the
intersection, we design a sequence model with the temporal convolutional
network (TCN) to capture the historical information and further merge it with
the spatial information to improve its performance. Extensive experiments
conducted in the multi-intersection scenario on synthetic data and real-world
data confirm that our method can achieve superior performance in travel time
and throughput against the state-of-the-art methods.
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