A Method of Predicting Short-term Traffic Flows Based on a DGC-GRU Model

CUI Wenyue; GU Yuanli; ZHAO Shengli; RUI Xiaoping

doi:10.3963/j.jssn.1674-4861.2023.02.013

Volume 41 Issue 2

Apr. 2023

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Article Navigation > Journal of Transport Information and Safety > 2023 > 41(2): 121-128

CUI Wenyue, GU Yuanli, ZHAO Shengli, RUI Xiaoping. A Method of Predicting Short-term Traffic Flows Based on a DGC-GRU Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 121-128. doi: 10.3963/j.jssn.1674-4861.2023.02.013

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CUI Wenyue, GU Yuanli, ZHAO Shengli, RUI Xiaoping. A Method of Predicting Short-term Traffic Flows Based on a DGC-GRU Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 121-128. doi: 10.3963/j.jssn.1674-4861.2023.02.013

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A Method of Predicting Short-term Traffic Flows Based on a DGC-GRU Model

doi: 10.3963/j.jssn.1674-4861.2023.02.013

1.
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
2.
Shanxi Taihangyunding Cultural Tourism Development Corporation, Jincheng 048000, Shanxi, China
3.
School of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China

Received Date: 2022-02-22
Available Online: 2023-06-19

Abstract

Abstract

In order to study the spatiotemporal characteristics of traffic flows on urban expressways which have not been fully explored in previous studies, a shortterm prediction method for traffic flow on urban expressways is proposed, in order to improve the prediction accuracy and efficiency based on a combined model of directed graph convolutional neural networks and gated recurrent units (DGC-GRU). The proposed method uses a spatial correlation matrix, which is also combined with a graph convolutional neural network. A directed graph convolutional neural network (DG-CNN) is developed to characterize the directionality and variability of traffic flows. Traffic flow parameters are input into the DG-CNN to obtain the directed graph convolution operator, and the directed graph convolution operator is introduced into the gated loop unit. The DG-CNN and the gated loop unit are used to capture spatial and temporal features of traffic flow, respectively, and are combined to predict traffic flow on expressways. Traffic flow data collected from a Ring Expressway of the City of Seattle is used for experiment analysis, in order to compare the performance of the proposed prediction models. Study results show that the convergence speed of the proposed DGC-GRU model is faster than other baseline models, and the mean absolute error (MAE) and mean absolute percentage error (MAPE) of the DGC-GRU model are smaller than those of the baseline models, given the same dataset and parameter settings. Compared with traditional GRU, GCN, and DGC-LSTM models, the DGC-GRU model reduces the MAE by 33.01%, 5.76%, 1.32%, and MAPE by 27.75%, 11.15%, 7.76%, respectively, which indicate that the DGC-GRU model can effectively study the spatiotemporal characteristics of traffic flows of the urban expressway network and has a better performance on prediction accuracy and efficiency than the compared models.
- urban expressway,
- short-term traffic flow forecasting,
- deep learning,
- graph convolution neural net-work,
- gate recurrent unit

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References(23)

References

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