Capturing Complex Spatial-Temporal Dependencies in Traffic Forecasting: A Self-Attention Approach
This work solves the problem of accurate and efficient traffic forecasting for urban planning and management, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles traffic forecasting by predicting region inflow/outflow, addressing the challenge of complex spatial-temporal dependencies that prior decoupled methods missed, and achieves an average improvement of up to 15% on RMSE, 17% on MAPE, and 32 times faster training time compared to state-of-the-art approaches.
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).