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Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part I

Research Article

Hyperparameter Analysis of Temporal Graph Convolutional Network Model Applied to Traffic Prediction

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  • @INPROCEEDINGS{10.1007/978-3-030-72792-5_53,
        author={Jing Huang and Lei Chen and Yuan An and Kailiang Zhang and Ping Cui},
        title={Hyperparameter Analysis of Temporal Graph Convolutional Network Model Applied to Traffic Prediction},
        proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part I},
        proceedings_a={SIMUTOOLS},
        year={2021},
        month={4},
        keywords={GCN Machine learning Hyperparameters optimization Traffic prediction},
        doi={10.1007/978-3-030-72792-5_53}
    }
    
  • Jing Huang
    Lei Chen
    Yuan An
    Kailiang Zhang
    Ping Cui
    Year: 2021
    Hyperparameter Analysis of Temporal Graph Convolutional Network Model Applied to Traffic Prediction
    SIMUTOOLS
    Springer
    DOI: 10.1007/978-3-030-72792-5_53
Jing Huang1, Lei Chen1,*, Yuan An1, Kailiang Zhang1, Ping Cui1
  • 1: Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology
*Contact email: chenlei@xzit.edu.cn

Abstract

GCN based on time and space is an essential part of smart city construction because it can capture the spatiotemporal dynamics and effectively analyze the traffic data to get the best prediction results. In the specific operation of the model, the adjustment and optimal selection of super parameters can make the model provide the best results, thus saving time, cost and computing power. When it comes to the prediction scenarios with low computational power and urgent demand, the existing super parameter search methods and optimization models lack efficiency and accuracy. Therefore, this paper proposes a super parameter search and optimization method based on cross validation, which can efficiently and accurately optimize the parameters, and select the best parameters by using the similarity between the learning and training errors corresponding to each super parameter To improve the prediction ability of the model. Through the verification of the actual data set, the model runs well, and can provide the best prediction results for the traffic flow and other scenarios dominated by spatiotemporal state.

Keywords
GCN Machine learning Hyperparameters optimization Traffic prediction
Published
2021-04-27
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-72792-5_53
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