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| Adaptive graph generation jump network for traffic flow prediction |
Jing HUANG1( ),Shu-yuan ZHONG1,Yuan-qiao WEN2,Kun LUO1 |
1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063, China
2. Intelligent Transportation System Research Center, Wuhan University of Technology, Wuhan 430063, China |
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Abstract A novel deep-learning-based model, adaptive graph generation jump network (AG-JNet), was proposed to solve the problem that traffic flow data has complex spatial-temporal correlations. The model consisted of two spatial-temporal modules, each of which was divided into two critical components, i.e., temporal correlation block and spatial correlation block. The temporal correlation block used multi-layer dilated convolution to increase the receptive field in temporal dimension while reducing computational cost. The spatial correlation block used adaptive graph generation convolution, which did not rely on the fixed graph structure to extract spatial correlation. Stacking multiple layers by jumping connections was used in both temporal and spatial modeling in order to improve the ability of extracting deep features of the model. The temporal feature and the spatial feature were fused by gated mechanism to obtain the spatial-temporal features for traffic flow prediction. Extensive experiments were conducted on two public datasets, i.e., PeMSD4 and PeMSD8. The experimental results showed that the AG-JNet achieved excellent performance under different traffic indicators.
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Received: 10 December 2020
Published: 27 October 2021
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| Fund: 国家自然科学基金资助项目(52072287)；武汉理工大学自主创新研究基金资助项目(205210016) |
用于交通流预测的自适应图生成跳跃网络
针对交通流数据复杂的时空相关性，提出新的基于深度学习的自适应图生成跳跃网络（AG-JNet模型）. 该模型由2个时空模块组成，每个时空模块分为2支，分别对时间相关性和空间相关性建模. 时间建模采用多层扩张卷积，在增大时间维度感受野的同时降低计算开销. 空间建模采用自适应图生成卷积，在不依赖图的固定结构下提取空间相关性. 在时间和空间的建模中均采用跳跃连接堆叠多层，以提升模型的深层特征提取能力，将时间特征和空间特征进行门控融合，提取出用于交通流量预测的时空特征. 在2个真实数据集PeMSD4和PeMSD8上的实验表明，AG-JNet在不同指标下取得了优异的性能.
关键词：
交通流量预测,
时空相关性,
自适应图生成,
扩张卷积,
跳跃连接
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