Attention-based STGNN with Long-Term Dependencies for Traffic Speed Prediction

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/95425

Title:	Attention-based STGNN with Long-Term Dependencies for Traffic Speed Prediction
Authors:	戴俊祐;Tai, Chun-Yu
Contributors:	資訊工程學系在職專班
Keywords:	交通車速預測;STGNN;Transformer;Huber Loss
Date:	2024-07-23
Issue Date:	2024-10-09 16:49:44 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著城市地區的發展，人口總數和密度不斷增加，導致都市化現象的形成。這使得交通網絡的規模擴大且結構變得複雜，進而加劇了交通擁塞問題。因此，準確預測交通速度對於城市交通網絡的管理和規劃至關重要。隨著現實道路複雜性的提高，如何整合空間和時間資訊以準確預測交通速度成為一個重要且具有挑戰性的研究課題。本研究提出了一種具有注意力機制的STGNN 模型，有效捕捉現實道路之間的複雜關係。我們使用 Huber 損失作為訓練損失函數以提高預測精度，並將 RMSNorm 用於替換Transformer 中的 LayerNorm，以降低計算成本。最後，我們在兩個真實世界的交通速度資料集上對所提出的模型進行了實驗。實證研究結果顯示，我們的方法在交通速度預測方面優於當前領域中最先進的系統。;Urbanization, characterized by the continuous growth of population and density in urban areas, has led to the expansion and complexity of transportation networks, exacerbating traffic congestion. Accurate traffic speed prediction is crucial for ef- fective traffic network management and planning. As the complexity of real-world roads increases, how to integrate spatial and temporal information for accurate traf- fic speed prediction has become a challenging research task. This study proposes a novel approach by introducing a novel spatial-temporal STGNN-based model to enhance the accuracy of traffic speed prediction. By employing an attention-based STGNN, we effectively capture the complex relationships among road segments in real-world scenarios. We utilize Huber loss as the training loss function to improve prediction accuracy. Finally, we replace LayerNorm in Transformer with RMSNorm to reduce computational costs. Using two real-world traffic speed datasets, we eval- uated the proposed model. The experimental results demonstrate that our method achieves superior performance compared to state-of-the-art traffic speed prediction works.
Appears in Collections:	[Executive Master of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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