ProSTformer: Pre-trained Progressive Space-Time Self-attention Model for Traffic Flow Forecasting

TL;DR

ProSTformer introduces a progressive space-time self-attention model that effectively captures spatiotemporal dependencies in traffic flow data, improving forecasting accuracy across various dataset scales.

Contribution

It proposes a novel factorized, progressive self-attention mechanism that incorporates spatiotemporal structure, reducing computation and enhancing traffic forecasting performance.

Findings

ProSTformer outperforms six state-of-the-art methods on large-scale datasets.

Pre-training on large datasets improves performance on smaller datasets.

The model effectively captures local to global spatial and inside-outside temporal dependencies.

Abstract

Traffic flow forecasting is essential and challenging to intelligent city management and public safety. Recent studies have shown the potential of convolution-free Transformer approach to extract the dynamic dependencies among complex influencing factors. However, two issues prevent the approach from being effectively applied in traffic flow forecasting. First, it ignores the spatiotemporal structure of the traffic flow videos. Second, for a long sequence, it is hard to focus on crucial attention due to the quadratic times dot-product computation. To address the two issues, we first factorize the dependencies and then design a progressive space-time self-attention mechanism named ProSTformer. It has two distinctive characteristics: (1) corresponding to the factorization, the self-attention mechanism progressively focuses on spatial dependence from local to global regions, on temporal dependence from inside to outside fragment (i.e., closeness, period, and trend), and finally on external dependence such as weather, temperature, and day-of-week; (2) by incorporating the spatiotemporal structure into the self-attention mechanism, each block in ProSTformer highlights the unique dependence by aggregating the regions with spatiotemporal positions to significantly decrease the computation. We evaluate ProSTformer on two traffic datasets, and each dataset includes three separate datasets with big, medium, and small scales. Despite the radically different design compared to the convolutional architectures for traffic flow forecasting, ProSTformer performs better or the same on the big scale datasets than six state-of-the-art baseline methods by RMSE. When pre-trained on the big scale datasets and transferred to the medium and small scale datasets, ProSTformer achieves a significant enhancement and behaves best.