ST-MLP: A Cascaded Spatio-Temporal Linear Framework with
  Channel-Independence Strategy for Traffic Forecasting
Zepu Wang Yuqi Nie Peng Sun Nam H. Nguyen John Mulvey H. Vincent Poor
The criticality of prompt and precise traffic forecasting in optimizing
traffic flow management in Intelligent Transportation Systems (ITS) has drawn
substantial scholarly focus. Spatio-Temporal Graph Neural Networks (STGNNs)
have been lauded for their adaptability to road graph structures. Yet, current
research on STGNNs architectures often prioritizes complex designs, leading to
elevated computational burdens with only minor enhancements in accuracy. To
address this issue, we propose ST-MLP, a concise spatio-temporal model solely
based on cascaded Multi-Layer Perceptron (MLP) modules and linear layers.
Specifically, we incorporate temporal information, spatial information and
predefined graph structure with a successful implementation of the
channel-independence strategy - an effective technique in time series
forecasting. Empirical results demonstrate that ST-MLP outperforms
state-of-the-art STGNNs and other models in terms of accuracy and computational
efficiency. Our finding encourages further exploration of more concise and
effective neural network architectures in the field of traffic forecasting.