具有可学习空间权重的图卷积网络在交通预测应用中的应用

IF 3.6 2区 工程技术 Q2 TRANSPORTATION
Bi Yu Chen , Yaohong Ma , Jiale Wang , Tao Jia , Xianglong Liu , William H. K. Lam
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引用次数: 0

摘要

如何为卷积图神经网络(convgnn)选择合适的空间加权方案是一个具有挑战性的问题。在本研究中,我们提出了一种称为可学习图卷积(LGC)网络的卷积神经网络,它学习道路及其k-hop邻居之间的空间权重作为空间卷积算子中的可学习参数。通过明确考虑空间权重在一天中不同时间的时间相关性,提出了一个动态LGC (DLGC)网络来学习空间权重的动态。建立了一种用于路网交通变量预测的多时相DLGC (MTDLGC)网络。实例研究结果表明,MT-DLGC网络比其他先进的基线具有更高的预测精度。LGC和DLGC网络都可以作为基准的一般空间加权方案,其预测性能优于现有的空间加权方案,如图关注。这项研究的源代码可以在https://github.com/Mayaohong/MTDLGC上公开获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph convolutional networks with learnable spatial weightings for traffic forecasting applications
How to select a suitable spatial weighting scheme for convolutional graph neural networks (ConvGNNs) is challenging. In this study, we propose a ConvGNN, termed learnable graph convolutional (LGC) network, which learns spatial weightings between a road and its k-hop neighbours as learnable parameters in the spatial convolutional operator. A dynamic LGC (DLGC) network is further proposed to learn the dynamics of spatial weightings by explicitly considering the temporal correlations of spatial weightings at different times of the day. A multi-temporal DLGC (MTDLGC) network is developed for forecasting traffic variables in road networks. Results of case study suggest that the MT-DLGC network can achieve higher prediction accuracy than other state-of-the-art baselines. Both LGC and DLGC networks can be used as general spatial weighting schemes for baselines with better forecasting performance than existing spatial weighting schemes, e.g., graph attention. The source code of this study is available publicly at https://github.com/Mayaohong/MTDLGC.
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来源期刊
Transportmetrica A-Transport Science
Transportmetrica A-Transport Science TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
8.10
自引率
12.10%
发文量
55
期刊介绍: Transportmetrica A provides a forum for original discourse in transport science. The international journal''s focus is on the scientific approach to transport research methodology and empirical analysis of moving people and goods. Papers related to all aspects of transportation are welcome. A rigorous peer review that involves editor screening and anonymous refereeing for submitted articles facilitates quality output.
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