Spatial-Temporal Attention Graph Convolution Network on Edge Cloud for Traffic Flow Prediction

Lai, Qifeng; Jing-zhen, Tian; Wang, Wei; Hu, Xiping

doi:10.1109/tits.2022.3185503

Cited by 15 publications

(4 citation statements)

References 38 publications

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“…2 , var, and accuracy of different hidden units under SZ-taxi testing set ▸ gated cycle unit structure (GRU) also decreased by 1.75%, which is the same in both data sets, indicating that STBGRN model method has more advantages in short-term traffic flow data processing, and can capture the spatiotemporal dependence. It can predict the traffic data from both time and space characteristics.…”

mentioning

confidence: 78%

“…Therefore, leveraging advanced data-driven technologies such as deep learning and machine learning to accurately predict traffic flow has become a focal point of current research. However, due to its complex spatiotemporal dependencies, traffic prediction demands simultaneous consideration of both temporal and spatial aspects, presenting persistent challenges [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

Zhang,

Xu,

Xiao

2024

Int J Comput Intell Syst

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With the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much attention. Efficient and accurate traffic flow prediction is one of the core tasks in the research of intelligent transportation system. Most of the existing spatiotemporal traffic flow prediction models do not make full use of various periodic spatiotemporal dynamic characteristics of traffic flow, and it is difficult to effectively capture the complex spatiotemporal changes of traffic flow. To accurately predict the traffic flow of complex urban network, this paper proposes a traffic prediction model (STBGRN) based on spatiotemporal bidirectional gated cycle unit (ST-BiGRU) and graph convolution residual network (GCN-ResNet). The spatiotemporal information in traffic data is learned using the spatiotemporal bidirectional GRU coupled with the forward and backward information of the current time step, and the topology structure of the traffic network is captured by the graph convolution residual network, which is combined to complete the prediction task of the urban traffic network. In this paper, STBGRN was tested on two real data sets, SZ-taxi and Los-loop, and compared with other baseline methods, the RMSE index reached 3.857 and 5.461, and the MAE index reached 2.702 and 3.781, respectively. Accuracy index is 72.66% and 91.35%, $${R}^{2}$$ R 2 index is 0.858 and 0.845, var index is 0.858 and 0.849. The experimental results show that STBGRN model can obtain spatiotemporal dependence from complex traffic data, and can be used in spatiotemporal traffic data processing.

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confidence: 78%

Section: Introductionmentioning

confidence: 99%

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

Zhang,

Xu,

Xiao

2024

Int J Comput Intell Syst

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“…Lai et al [36] introduced the NodeRank algorithm to calculate the importance of road nodes based on extracting the spatiotemporal features of traffic flow prediction tasks.…”

Section: Preliminaries a Related Workmentioning

confidence: 99%

“…Main highlights Zhao et al [33] Exploring the performance of traffic prediction from both temporal and spatial dimensions Wang et al [34] Using spatial layer to extract spatial relationships between traffic networks Wang et al [35] Incorporating the Adjacent Similar algorithm to predict traffic flow at intersections without historical data Lai et al [36] Introducing the NodeRank algorithm to calculate the road importance based on spatiotemporal features Qi et al [37] Designing a cloud model to aggregate the global parameters of each submodel Zheng et al [38] Sequentially inputting traffic data into several neural computing structures to obtain prediction results…”

Section: Referencesmentioning

confidence: 99%

A Graph Deep Learning-Based Fast Traffic Flow Prediction Method in Urban Road Networks

Yang,

2023

IEEE Access

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In modern smart cities, road networks are becoming more and more complicated, resulting in more complex format of graphs. This brings many challenges to the forecasting of traffic flow in road graphs. Most of traditional traffic flow forecasting methods ignored many implicit relationships inside road graphs. And this cannot be well suitable for modern road networks in smart cities. Besides, the operation of smart cities is accompanied with real-time big data stream. The running efficiency of forecasting methods is another important concern. To handle this issue, this paper proposes a graph deep learning-based fast traffic flow forecasting method in urban road networks. Firstly, the theory about graph convolution operations is deduced and can be used as the basis of a graph convolution network (GCN). Then, the whole road network is viewed as a complex road graph, and the GCN is introduced to establish a novel forecasting method for graph-level traffic flow. With roads being regarded as nodes and their relations being regarded as edges, graph-level forecasting can be realized with the use of the proposed method. Experiments are carried out on a standard real dataset to evaluate the proposal. The experimental results show a proper performance of the proposal.

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Principal graph embedding convolutional recurrent network for traffic flow prediction

et al. 2023

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Spatial-Temporal Attention Graph Convolution Network on Edge Cloud for Traffic Flow Prediction

Cited by 15 publications

References 38 publications

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

A Graph Deep Learning-Based Fast Traffic Flow Prediction Method in Urban Road Networks

Principal graph embedding convolutional recurrent network for traffic flow prediction

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