| Computer Technology |
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| Urban traffic flow prediction algorithm based on graph convolutional neural networks |
Xu YAN( ),Xiao-liang FAN*(),Chuan-pan ZHENG,Yu ZANG,Cheng WANG,Ming CHENG,Long-biao CHEN |
| School of Informatics, Xiamen University, Xiamen 361000, China |
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Abstract An improved spatio-temporal graph convolutional networks traffic prediction algorithm, named free-flow reachable matrix-based spatio-temporal graph convolutional networks (FAST-GCN), was proposed, in order to predict real-time traffic flows accurately and improve the sensing and prediction of citywide traffic situation. The characteristics of urban complex road network structure were expressed effectively by the graph convolutional neural network, and the spatio-temporal dependency in complex road networks was explored by introducing free-flow reachable matrices. Thus the accuracy of traffic situation prediction was improved. First, preprocess traffic speeds and sensors location data. Second, with the existing spatio-temporal graph convolutional networks, the graph convolution module based on free flow reachable matrix was integrated to effectively capture the unique spatial characteristics of the urban traffic road networks. Finally, the prediction results were generated through a fully connected output layer. The proposed model was evaluated on a real-world traffic dataset PeMS. The experimental results show that this model could capture physical characteristics of road network and spatio-temporal dependency, and outperform the baselines such as spatio-temporal graph convolutional networks (STGCN), and the prediction accuracy in 45 minutes was improved by up to 5.656%. In addition, compared with baselines, the proposed model can adapt to traffic flow prediction in large-scale road networks and has superior scalability.
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Received: 03 January 2020
Published: 06 July 2020
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Corresponding Authors:
Xiao-liang FAN
E-mail: yanxu97@stu.xmu.edu.cn;fanxiaoliang@xmu.edu.cn
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基于图卷积神经网络的城市交通态势预测算法
为了实时准确地预测城市交通流量，提高城市交通态势感知和预测准确度，提出一种改进的时空图卷积深度神经网络算法：基于自由流动可达矩阵的时空图卷积深度神经网络（FAST-GCN）. 利用图卷积神经网络有效表达城市复杂路网的结构特性，引入自由流动可达矩阵来挖掘复杂路网的时空依赖性，从而提高交通态势预测准确度；对交通流速及站点地理位置数据进行数据预处理；在现有的时空图卷积深度神经网络算法的基础上，增加基于自由流动可达矩阵的图卷积模块，以有效挖掘城市交通路网的独特空间特征；通过一个全连接的输出层输出交通流预测结果；在真实世界数据集PeMS上对算法效果进行验证. 结果表明，采用提出的FAST-GCN算法能够有效获取交通路网独特的物理特性，从而捕获交通数据的时空依赖性，优于时空图卷积（STGCN）等基线算法，其在45 min的预测准确率最好可提高5.656%；相比基线模型，所提算法能够适应大规模路网的交通流预测，且具有可扩展性.
关键词：
交通流预测,
深度学习,
图卷积神经网络,
时空依赖性,
自由流动可达矩阵
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