A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting

Guanyao Li, Shuhan Zhong, Xingdong Deng, Letian Xiang, S.-H. Gary Chan, Ruiyuan Li, Yang Liu, Ming Zhang, Chih-Chieh Hung, Wen-Chih Peng

Abstract: We study the forecasting problem for traffic with dynamic, possibly periodical, and joint spatial-temporal dependency between regions. Given the aggregated inflow and outflow traffic of regions in a city from time slots 0 to $t - 1$ , we predict the traffic at time $t$ for any region. Prior arts in the area often considered the spatial and temporal dependencies in a decoupled manner, or were rather computationally intensive in training with a large number of hyper-parameters which needed tuning. We propose ST-TIS, a novel, lightweight and accurate S patial- T emporal T ransformer with i nformation fusion and region s ampling for traffic forecasting. ST-TIS extends the canonical Transformer with information fusion and region sampling. The information fusion module captures the complex spatial-temporal dependency between regions. The region sampling module is to improve the efficiency and prediction accuracy, cutting the computation complexity for dependency learning from $O(n^{2})$ to $O(n\sqrt{n})$ , where $n$ is the number of regions. With far fewer parameters than state-of-the-art deep learning models, ST-TIS's offline training is significantly faster in terms of tuning and computation (with a reduction of up to $90\%$ on training time and network parameters). Notwithstanding such training efficiency, extensive experiments show that ST-TIS is substantially more accurate in online prediction than state-of-the-art approaches (with an average improvement of $9.5\%$ on RMSE, and $12.4\%$ on MAPE compared to STDN and DSAN).