MLPST: MLP is All You Need for Spatio-Temporal Prediction
This addresses the need for efficient and lightweight traffic prediction methods for public transportation systems, though it is incremental as it builds on existing MLP-based approaches.
The authors tackled traffic prediction by proposing MLPST, a pure multi-layer perceptron framework that captures spatial and temporal dependencies with linear computational complexity and model parameters over an order of magnitude lower than baselines, achieving superior accuracy and efficiency in experiments.
Traffic prediction is a typical spatio-temporal data mining task and has great significance to the public transportation system. Considering the demand for its grand application, we recognize key factors for an ideal spatio-temporal prediction method: efficient, lightweight, and effective. However, the current deep model-based spatio-temporal prediction solutions generally own intricate architectures with cumbersome optimization, which can hardly meet these expectations. To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction. Specifically, we first capture spatial relationships from both local and global receptive fields. Then, temporal dependencies in different intervals are comprehensively considered. Through compact and swift MLP processing, MLPST can well capture the spatial and temporal dependencies while requiring only linear computational complexity, as well as model parameters that are more than an order of magnitude lower than baselines. Extensive experiments validated the superior effectiveness and efficiency of MLPST against advanced baselines, and among models with optimal accuracy, MLPST achieves the best time and space efficiency.