Minimalist Traffic Prediction: Linear Layer Is All You Need
- URL: http://arxiv.org/abs/2308.10276v2
- Date: Wed, 23 Aug 2023 10:10:49 GMT
- Title: Minimalist Traffic Prediction: Linear Layer Is All You Need
- Authors: Wenying Duan, Hong Rao, Wei Huang, Xiaoxi He
- Abstract summary: Traffic prediction is essential for the progression of Intelligent Transportation Systems (ITS) and the vision of smart cities.
STGNNs have shown promise in this domain by leveraging Graph Neural Networks (GNNs) integrated with either RNNs or Transformers.
This paper addresses these challenges, advocating for three main solutions: a node-embedding approach, time series decomposition, and periodicity learning.
- Score: 9.677531063090104
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Traffic prediction is essential for the progression of Intelligent
Transportation Systems (ITS) and the vision of smart cities. While
Spatial-Temporal Graph Neural Networks (STGNNs) have shown promise in this
domain by leveraging Graph Neural Networks (GNNs) integrated with either RNNs
or Transformers, they present challenges such as computational complexity,
gradient issues, and resource-intensiveness. This paper addresses these
challenges, advocating for three main solutions: a node-embedding approach,
time series decomposition, and periodicity learning. We introduce STLinear, a
minimalist model architecture designed for optimized efficiency and
performance. Unlike traditional STGNNs, STlinear operates fully locally,
avoiding inter-node data exchanges, and relies exclusively on linear layers,
drastically cutting computational demands. Our empirical studies on real-world
datasets confirm STLinear's prowess, matching or exceeding the accuracy of
leading STGNNs, but with significantly reduced complexity and computation
overhead (more than 95% reduction in MACs per epoch compared to
state-of-the-art STGNN baseline published in 2023). In summary, STLinear
emerges as a potent, efficient alternative to conventional STGNNs, with
profound implications for the future of ITS and smart city initiatives.
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