MLPST: MLP is All You Need for Spatio-Temporal Prediction

TL;DR

This paper introduces MLPST, a simple yet effective multi-layer perceptron framework for traffic spatio-temporal prediction, emphasizing efficiency, lightweight design, and competitive accuracy.

Contribution

The paper presents a novel pure MLP architecture for traffic prediction that captures spatial and temporal dependencies with low complexity and high efficiency.

Findings

MLPST achieves superior accuracy compared to advanced baselines.

MLPST requires significantly fewer parameters and less computation.

Extensive experiments validate its effectiveness and efficiency.

Abstract

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.