Adaptive graph convolutional network-based short-term passenger flow prediction for metro
Document Type
Journal Article
Publication Date
2024
Subject Area
place - asia, place - urban, mode - subway/metro, ridership - demand, ridership - modelling, land use - impacts, infrastructure - station
Keywords
Deep learning, graph convolutional network, metro card swiping data, short-term passenger flow prediction, urban traffic
Abstract
With the development and acceleration of urbanization, urban metro traffic is gradually growing up to a large network, and the structure of topology between stations becomes more complex, which makes it increasingly difficult to capture the spatial dependency. The vertical and horizontal interlacing of multiple lines makes the stations distributed topologically, and the traditional graph convolution is implemented on the adjacency matrix generated based on a priori knowledge, which cannot reflect the actual spatial dependence between stations. To address these problems, this paper proposes an adaptive graph convolutional network model (Adapt-GCN), which replaces the fixed adjacency matrix obtained from a priori knowledge in the traditional GCN with a trainable adaptive adjacency matrix. This can not only effectively adjust the weights of correlations between adjacent stations, but also adaptively capture the spatial dependencies between non-adjacent stations. This paper uses the Shanghai Metro dataset to verify the effectiveness of the model in improving prediction accuracy and reducing training time.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
Zhao, J., Zhang, R., Sun, Q., Shi, J., Zhuo, F., & Li, Q. (2024). Adaptive graph convolutional network-based short-term passenger flow prediction for metro. Journal of Intelligent Transportation Systems, 28(6), 806-815.
