Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit

Dewen SENG; Fanshun LV; Ziyi LIANG; Xiaoying SHI; Qiming FANG

doi:10.1631/FITEE.2000243

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Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit

Regular Papers | Updated：2022-05-19

- Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit
- 基于多图卷积网络和门控循环单元的不规则区域交通流量预测
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 9, Pages: 1179-1193(2021)
- Affiliations：
  
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
- Author bio：
  
  [ "Dewen SENG, E-mail: sengdw@hdu.edu.cn" ]
  [ "Fanshun LV, E-mail: 172050041@hdu.edu.cn" ]
  [ "Ziyi LIANG, E-mail: liangziyi2020@163.com" ]
  Xiaoying SHI, E-mail: shixiaoying@hdu.edu.cn
  [ "Qiming FANG, E-mail: fangqiming@hdu.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.2000243
  CLC： TP391
- Received：21 May 2020，
  
  Revised：2021-;4-;1，
  
  Published Online：29 July 2021，
  
  Published：2021-09
- Accepted：
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Dewen SENG, Fanshun LV, Ziyi LIANG, et al. Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(9): 1179-1193.
DOI：

Dewen SENG, Fanshun LV, Ziyi LIANG, et al. Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(9): 1179-1193. DOI： 10.1631/FITEE.2000243.

摘要

区域交通流量预测对智能交通系统的交通控制和管理十分重要。借助深度神经网络，采用仅适用于规则网格的循环神经网络或残差神经网络捕获流量预测的空间依赖性。但是，考虑到路网和行政边界得到的区域通常是不规则的。因此将城市划分成网格进行预测是不准确的。提出一种基于多图卷积网络和门控循环单元（MGCN-GRU）的不规则区域交通流量预测模型。首先，构建一个城市异质区域间关联图反映各区域间的关联。在每个图中，节点表示不规则区域，边代表区域间的关联类型。然后，提出一个多图卷积网络融合不同区域间关联图和附加属性。进一步采用门控循环单元捕获时序依赖并预测未来交通流量。实验结果表明，基于3个真实大数据集（公共自行车系统数据集、出租车数据集和无桩共享自行车数据集），所提MGCN-GRU模型性能优于多个现有方法。

Abstract

The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system. With the help of deep neural networks

the convolutional neural network or residual neural network

which can be applied only to regular grids

is adopted to capture the spatial dependence for flow prediction. However

the obtained regions are always irregular considering the road network and administrative boundaries; thus

dividing the city into grids is inaccurate for prediction. In this paper

we propose a new model based on multi-graph convolutional network and gated recurrent unit (MGCN-GRU) to predict traffic flows for irregular regions. Specifically

we first construct heterogeneous inter-region graphs for a city to reflect the relationships among regions. In each graph

nodes represent the irregular regions and edges represent the relationship types between regions. Then

we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes. The GRU is further used to capture the temporal dependence and to predict future traffic flows. Experimental results based on three real-world large-scale datasets (public bicycle system dataset

taxi dataset

and dockless bike-sharing dataset) show that our MGCN-GRU model outperforms a variety of existing methods.

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