Adaptive graph convolutional network-based short-term passenger flow prediction for metro

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.

Keywords:

• Deep learning
• graph convolutional network
• metro card swiping data
• short-term passenger flow prediction
• urban traffic

Acknowledgments

This work was supported by the [Natural Science Foundation of Shandong Province #1] under Grant [number ZR2021MF113, number ZR2021MF104]; [National Natural Science Foundation #2] under Grant [number 62072288]; [Key R&D Projects of Qingdao Science and Technology Plan #3] under Grant [number 21-1-2-19-xx]; [Qingdao West Coast New District Science and Technology Plan #4] under Grant [number 2020-1-6] and [Innovation Ability Improvement Project of Small and Mediumsized Sci-tech Enterprises #5] under Grant [number 2020TSGC1084].

Disclosure statement

No potential conflict of interest was reported by the author(s).