A Hyperparameters automatic optimization method of time graph convolution network model for traffic prediction

Chen, Lei; Bei, Lulu; An, Yuan; Zhang, Kailiang; Cui, Ping

doi:10.1007/s11276-021-02672-5

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…The simpler single-model methods are introduced firstly, which typically focus on capturing features from specific spatial or temporal dimensions. In capturing spatial correlation features, early research heavily relied on convolutional neural networks (CNN) due to their advantages in local perception [20][21], weight sharing [22][23], and capability in grasping European spatial correlation features [24][25][26][27]. Given the irregular distribution of traffic data, graph convolutional neural networks (GCNs) that emphasize non-Euclidean spatial feature extraction have increasingly gained traction over CNNs.…”

Section: Related Workmentioning

confidence: 99%

“…GCNs capture the non-Euclidean spatial correlation traits between various nodes [27]. > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < Other notable works include those by Li et al [28], who introduced the DCRNN model, Chen et al's [21] regression analysis with GCN, Jepsen et al's [22] innovations in road network attributes, and the adaptive adjacency matrix mechanism in GCN proposed by Yao et al [20] and An et al [16].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Chu,

Hou,

Jiang

et al. 2024

IEEE Trans. Intell. Veh.

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Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this paper, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Chu,

Hou,

Jiang

et al. 2024

IEEE Trans. Intell. Veh.

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show abstract

“…It is found that the potential spatiotemporal evolution rules and characteristics of traffic states from massive data, which have become the mainstream for traffic state analysis [10,11]. Traffic data is critical for accurate traffic prediction [12]. It is a spatiotemporal dataset with spatial structure differences and dynamic changes over time.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Spatiotemporal Optimization Method of Hypergraph Convolution Networks for Traffic Prediction

Zhu,

Gong

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Traffic prediction is a necessary function in intelligent transportation systems to alleviate traffic congestion. Graph learning methods mainly focus on the spatiotemporal dimension, but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments. There exist two issues: 1) deep integration of the spatiotemporal information and 2) global spatial dependencies for structural properties. To address these issues, we propose a nonlinear spatiotemporal optimization method, which introduces hypergraph convolution networks (HGCN). The method utilizes the higher-order spatial features of the road network captured by HGCN, and dynamically integrates them with the historical data to weigh the influence of spatiotemporal dependencies. On this basis, an extended Kalman filter is used to improve the accuracy of traffic prediction. In this study, a set of experiments were conducted on the real-world dataset in Chengdu, China. The result showed that the proposed method is feasible and accurate by two different time steps. Especially at the 15-minute time step, compared with the second-best method, the proposed method achieved 3.0%, 11.7%, and 9.0% improvements in RMSE, MAE, and MAPE, respectively.

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“…CNN has good ability of extracting spatial feature when Euclidean data structure is employed, but it cannot directly process non-Euclidean structure data [ 28 ]. To extract spatial characteristics from data of non-Euclidean structure, motivated by CNN [ 29 ], Graph Neural Network (GNN) [ 30 ] and Graph Convolutional Network (GCN) [ 31 ] had been proposed to investigate complex spatial topological structure. The methods mentioned above only focused on the spatial feature extraction but neglected the important temporal features.…”

Section: Introductionmentioning

confidence: 99%

Global-Local Spatial-Temporal Residual Correlation Network for Urban Traffic Status Prediction

Bao

Cao

Shen

et al. 2022

Computational Intelligence and Neuroscience

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The recent proposed Spatial-Temporal Residual Network (ST-ResNet) model is an effective tool to extract both spatial and temporal characteristics and has been successfully applied to urban traffic status prediction. However, the ST-ResNet model only extracts the local spatial characteristics and ignores the very important global spatial characteristics. In this paper, a novel Global-Local Spatial-Temporal Residual Correlation Network (GL-STRCN) model is proposed for urban traffic status prediction to further improve the prediction accuracy of the existing ST-ResNet model. The GL-STRCN model firstly applies Pearson’s correlation coefficient method to extract high correlation series. Then, considering both global and local spatial properties, two components consisting of 2D convolution and residual operation are used to capture spatial features. After that, based on Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), a novel long-term temporal feature extraction component is proposed to capture temporal features. Finally, the spatial and temporal features are aggregated together in a weighted way for final prediction. Experiments have also been performed using two datasets from TaxiCD and PEMS-BAY. The results indicated that the proposed model produces a better prediction performance compared with the results based on other baseline solutions, e.g., CNN, ST-ResNet, GL-TCN, and DGLSTNet.

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A Hyperparameters automatic optimization method of time graph convolution network model for traffic prediction

Cited by 7 publications

References 32 publications

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

A Nonlinear Spatiotemporal Optimization Method of Hypergraph Convolution Networks for Traffic Prediction

Global-Local Spatial-Temporal Residual Correlation Network for Urban Traffic Status Prediction

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