用自注意力机制联合建模交通时空依赖，提升预测精度与效率。
Capturing Complex Spatial-Temporal Dependencies in Traffic Forecasting: A Self-Attention Approach
- 通过区域嵌入与自注意力机制联合捕捉远近区域的时空关联。
- 在两个真实数据集上，均方根误差降低15%，平均绝对百分比误差下降17%。
- 适合需要高效高精度交通预测的智能交通系统开发者使用。
本文研究交通预测问题，旨在预测区域在未来时间片内的进出流量。该问题复杂源于区域间复杂的时空依赖关系。以往方法通常分步处理空间与时间依赖，未能捕捉其协同效应。为此，本文提出ST-SAM：一种新颖高效的时空自注意力模型。该模型首先通过区域嵌入层从交通数据中学习时间特定的区域表示；随后利用基于自注意力机制的时空依赖学习模块，捕获近邻与远距离区域的联合时空依赖。整个模型完全依赖自注意力机制，有效捕捉局部与全局相关性，兼具高效性与准确性。在两个真实世界数据集上的大量实验表明，相较于当前最优方法，ST-SAM在均方根误差（RMSE）上平均提升达15%，平均绝对百分比误差（MAPE）降低17%，训练时间缩短32倍。
原文摘要 · Abstract (English)
We study the problem of traffic forecasting, aiming to predict the inflow and outflow of a region in the subsequent time slot. The problem is complex due to the intricate spatial and temporal interdependence among regions. Prior works study the spatial and temporal dependency in a decouple manner, failing to capture their joint effect. In this work, we propose ST-SAM, a novel and efficient Spatial-Temporal Self-Attention Model for traffic forecasting. ST-SAM uses a region embedding layer to learn time-specific embedding from traffic data for regions. Then, it employs a spatial-temporal dependency learning module based on self-attention mechanism to capture the joint spatial-temporal dependency for both nearby and faraway regions. ST-SAM entirely relies on self-attention to capture both local and global spatial-temporal correlations, which make it effective and efficient. Extensive experiments on two real world datasets show that ST-SAM is substantially more accurate and efficient than the state-of-the-art approaches (with an average improvement of up to 15% on RMSE, 17% on MAPE, and 32 times on training time in our experiments).
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