公路事故延迟时间预测的双注意多尺度图卷积网络

I-Ying Wu, Fandel Lin, Hsun-Ping Hsieh
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引用次数: 0

摘要

与传统方法不同,交通相关预测在制定交通政策方面发挥着关键作用,传统方法只能根据统计结果或历史经验做出决策。通过机器学习,我们能够捕捉城市动态之间潜在的相互作用,并在空间环境中找到它们之间的相互作用。然而,尽管有大量与交通相关的研究,但很少有研究探讨预测拥堵的影响。因此,本文的重点是预测车祸如何导致交通拥堵,特别是拥堵发生所需的时间。因此,我们提出了一种新的模型——双注意多尺度图卷积网络(DAMGNet)来解决这个问题。在该模型中,考虑并结合了事故信息、城市动态和各种公路网特征等异构数据。接下来,上下文编码器对事故数据进行编码,空间编码器捕获多尺度图卷积网络(GCNs)之间的隐藏特征。利用我们设计的双注意机制,DAMGNet模型能够有效地学习特征之间的相关性。在真实数据集上进行的评估证明,我们的DAMGNet在RMSE和MAE方面比其他比较方法有显着改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dual-Attention Multi-Scale Graph Convolutional Networks for Highway Accident Delay Time Prediction
Traffic-related forecasting plays a critical role in determining transportation policy, unlike traditional approaches, which can only make decisions based on statistical results or historical experience. Through machine learning, we are able to capture the potential interactions between urban dynamics and find their mutual interactions in a spatial context. However, despite a plethora of traffic-related studies, few works have explored predicting the impact of congestion. Therefore, this paper focuses on predicting how a car accident leads to traffic congestion, especially the length of time it takes for the congestion to occur. Accordingly, we propose a novel model named Dual-Attention Multi-Scale Graph Convolutional Networks (DAMGNet) to address this issue. In this proposed model, heterogeneous data such as accident information, urban dynamics, and various highway network characteristics are considered and combined. Next, the context encoder encodes the accident data, and the spatial encoder captures the hidden features between multi-scale Graph Convolutional Networks (GCNs). With our designed dual attention mechanism, the DAMGNet model is able to effectively learn the correlation between features. The evaluations conducted on a real-world dataset prove that our DAMGNet has a significant improvement in RMSE and MAE over other comparative methods.
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