论文标题
可解释的人群流以时空的自我注意力预测
Interpretable Crowd Flow Prediction with Spatial-Temporal Self-Attention
论文作者
论文摘要
人群流量预测越来越多地在智能城市计算领域中作为城市管理系统的基本组成部分进行了研究。预测人群流量的最具挑战性的部分是衡量复杂的时空依赖性。当前方法中采用的一种普遍的解决方案是将空间和时间信息划分为各种架构（例如CNN/GCN，LSTM）。但是，该策略有两个缺点：（1）复杂的依赖性也被分割，因此部分隔离； （2）在通过不同的体系结构时，时空特征将变成潜在表示，从而使预测的人群流动变得很难。为了解决这些问题，我们提出了一个使用ST编码门的时空自我发项网络（Stsan），该网络编码门可以用位置和时间编码计算整个时空表示，因此避免了依赖关系。此外，我们开发了一种多相关的注意机制，该机制在时空信息上应用了缩放的点产物注意力，并测量明确指示依赖项的注意力权重。交通和移动数据的实验结果表明，与SOTA基线相比，出租车NYC数据集的流入和流出RMSE的流入和流出RMSE减少了16％和8％。
Crowd flow prediction has been increasingly investigated in intelligent urban computing field as a fundamental component of urban management system. The most challenging part of predicting crowd flow is to measure the complicated spatial-temporal dependencies. A prevalent solution employed in current methods is to divide and conquer the spatial and temporal information by various architectures (e.g., CNN/GCN, LSTM). However, this strategy has two disadvantages: (1) the sophisticated dependencies are also divided and therefore partially isolated; (2) the spatial-temporal features are transformed into latent representations when passing through different architectures, making it hard to interpret the predicted crowd flow. To address these issues, we propose a Spatial-Temporal Self-Attention Network (STSAN) with an ST encoding gate that calculates the entire spatial-temporal representation with positional and time encodings and therefore avoids dividing the dependencies. Furthermore, we develop a Multi-aspect attention mechanism that applies scaled dot-product attention over spatial-temporal information and measures the attention weights that explicitly indicate the dependencies. Experimental results on traffic and mobile data demonstrate that the proposed method reduces inflow and outflow RMSE by 16% and 8% on the Taxi-NYC dataset compared to the SOTA baselines.
