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ST-GMLP: A concise spatial-temporal framework based on gated multi-layer perceptron for traffic flow forecasting
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AI
DOI：10.1016/j.neunet.2024.107074.png)
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Abstract
En 中文
The field of traffic forecasting has been the subject of considerable attention as a critical component in alleviating traffic congestion and improving urban services. Given the regular patterns of human activities, it is evident that traffic flow is inherently periodic. However, most of existing studies restrict themselves to recent historical observations and typically yield structurally and computationally complex models, which greatly limits the forecasting accuracy and hinders the application of models in realistic situations. To this end, this paper proposes a concise framework named Spatial-Temporal Gated Multi-Layer Perceptron (ST-GMLP), aiming to enhance the forecasting performance by leveraging the temporal patterns of different scales with a simple and effective structure. Nevertheless, due to the incorporation of more historical features, the presence of distribution shifts between periods further restricts the forecasting accuracy. To address the above issue, ST-GMLP employs a parallel structure of learning the interdependencies of traffic flow in both spatial node and temporal directions, and then establishes the interactions between time and space to effectively mitigate the adverse effects due to temporal distribution shifts. Owing to the utilization of MLP with gated mechanisms (GMLP) for modeling the spatial-temporal interdependencies, ST-GMLP has significant advantages in terms of training efficiency and resources occupation. Extensive experimental findings indicate that ST-GMLP exhibits superior performance in comparison to state-of-the-art methods.
Keywords:
Spatial-temporal data
Deep learning
Multi-layer perceptron
Gated mechanisms
Traffic prediction
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