交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (4): 49-55.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于图卷积网络的路网短时交通流预测研究

陈喜群*,周凌霄,曹 震   

  1. 浙江大学 建筑工程学院,杭州 310058
  • 收稿日期:2020-04-01 修回日期:2020-05-14 出版日期:2020-08-25 发布日期:2020-08-25
  • 作者简介:陈喜群(1986-),男,黑龙江人,研究员,博士.
  • 基金资助:

    国家重点研发计划/National Key Research and Development Program of China (2018YFB1600900);国家自然科学基金/National Natural Science Foundation of China (71922019, 71771198).

Short-term Network-wide Traffic Prediction Based on Graph Convolutional Network

CHEN Xi-qun, ZHOU Ling-xiao, CAO Zhen   

  1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
  • Received:2020-04-01 Revised:2020-05-14 Online:2020-08-25 Published:2020-08-25

摘要:

智能交通系统是缓解交通拥堵行之有效的手段,精准的交通流预测是其实现的关键所在. 本文考虑路网拓扑结构和交通流时空相关性,提出基于图卷积网络(Graph Convolution Network,GCN)的大规模城市路网短时交通流预测模型,具有较高的预测精度、预测效率和现实解释意义;采用真实大规模城市路网浮动车数据对GCN模型进行测试,结果表明,GCN模型相对于现有模型,在预测性能上有较大提升.

关键词: 智能交通, 短时交通流预测, 图卷积网络, 城市路网, 深度学习

Abstract:

Intelligent transportation systems provide an effective means to alleviate traffic congestion. Traffic flow prediction is the key to realize it. This paper proposes a short-term traffic flow prediction model for largescale urban road networks based on graph convolutional network (GCN). The topological structure of the road network is considered as well as the spatial-temporal correlation of traffic flow, which results in high prediction accuracy, high efficiency, and interpretability of the model. A case study was performed on the model using realworld large-scale urban road network data. The results show that the GCN model greatly improves the prediction performance compared to existing benchmarks.

Key words: intelligent transportation, short- term traffic prediction, graph convolutional network, urban road network;deep learning

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