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 at any region. Prior arts in the area often consider the spatial and temporal dependencies in a decoupled manner or are rather computationally intensive in training with a large number of hyper-parameters to tune. We propose ST-TIS, a novel, lightweight, and accurate Spatial-Temporal Transformer with information fusion and region sampling 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 models, the offline training of our model 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 up to $9.5\%$ on RMSE, and $12.4\%$ on MAPE).
翻译:我们以动态的、可能的定期的和联合的空间-时际依赖性来研究交通的预测问题;鉴于一个城市从0点到1点的区域总流量和流出流量,我们预测任何区域的时间流量;该地区以前的艺术经常以脱钩的方式考虑空间和时间依赖性,或者在大量超参数进行调和的培训时进行计算密集;我们提议ST-TIS,是一个新颖的、轻便的和准确的时空变异器,为交通预测提供信息融合和区域抽样;ST-TIS以信息融合和区域抽样方式扩展罐式变异器;信息融合模块捕捉到各区域之间复杂的空间-时际依赖性;区域取样模块的目的是提高效率和预测准确性,将依赖性学习的计算复杂性从$(n%2美元)降低到$(nscrt{n)美元,这是各区域的数目;ST-TIS的参数远比最新水平模型和区域取样率要少得多;ST-TI的离线性培训在网络上大大的升级和不断进行时间化的实验,在网络上显示不断进行这种递减速度和不断进行98的实验。