Capturing Complex Spatial-Temporal Dependencies in Traffic Forecasting: A Self-Attention Approach

• URL: http://arxiv.org/abs/2511.07980v1
• Date: Wed, 12 Nov 2025 01:32:15 GMT
• Title: Capturing Complex Spatial-Temporal Dependencies in Traffic Forecasting: A Self-Attention Approach
• Authors: Zheng Chenghong, Zongyin Deng, Liu Cheng, Xiong Simin, Di Deshi, Li Guanyao,
• Abstract summary: We study the problem of traffic forecasting, aiming to predict the inflow and outflow of a region in the subsequent time slot.<n>The problem is complex due to the intricate spatial and temporal interdependence among regions.<n>We propose ST-SAM, a novel and efficient Spatial-Temporal Self-Attention Model for traffic forecasting.
• Score: 0.5541644538483947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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