{"ID":2843241,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07980","arxiv_id":"2511.07980","title":"Capturing Complex Spatial-Temporal Dependencies in Traffic Forecasting: A Self-Attention Approach","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).","short_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 th...","url_abs":"https://arxiv.org/abs/2511.07980","url_pdf":"https://arxiv.org/pdf/2511.07980v1","authors":"[\"Zheng Chenghong\",\"Zongyin Deng\",\"Liu Cheng\",\"Xiong Simin\",\"Di Deshi\",\"Li Guanyao\"]","published":"2025-11-11T08:46:00Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}