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Application of an Adaptive Adjacency Matrix-Based Graph Convolutional Neural Network in Taxi Demand Forecasting

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  • Jian-You Xu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Shuo Zhang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Chin-Chia Wu

    (Department of Statistics, Feng Chia University, Taichung 40724, Taiwan)

  • Win-Chin Lin

    (Department of Statistics, Feng Chia University, Taichung 40724, Taiwan)

  • Qing-Li Yuan

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

Accurate forecasting of taxi demand has facilitated the rational allocation of urban public transport resources, reduced congestion in urban transport networks, and shortened passenger waiting time. However, virtual station discovery and modelling of the demand when forecasting through graph convolutional neural networks remains challenging. In this study, the virtual station discovery problem was addressed by using a two-stage clustering approach, which considers the geographical and load characteristics of taxi demand. Furthermore, a fusion model combining non-negative matrix decomposition and a graph convolutional neural network was proposed in order to extract the features of the nodes for dimension reduction and adaptive adjacency matrix computation. By the construction of a local processing structure, further extraction of the local characteristics of the demand was achieved. The experimental results show that the method in this study outperforms state-of-the-art methods in terms of the root mean square error and average absolute value error. Therefore, the model proposed in this study is able to achieve accurate forecasting of taxi demand.

Suggested Citation

  • Jian-You Xu & Shuo Zhang & Chin-Chia Wu & Win-Chin Lin & Qing-Li Yuan, 2022. "Application of an Adaptive Adjacency Matrix-Based Graph Convolutional Neural Network in Taxi Demand Forecasting," Mathematics, MDPI, vol. 10(19), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3694-:d:936831
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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