Passenger Flow Prediction of Scenic Spot Using a GCN–RNN Model

Zhijie Xu, Liyan Hou, Yueying Zhang and Jianqin Zhang
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Zhijie Xu: School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Liyan Hou: School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Yueying Zhang: Artificial Intelligence College, Baoding University, Baoding 071000, China
Jianqin Zhang: School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

Sustainability, 2022, vol. 14, issue 6, 1-14

Abstract: The prediction and control of passenger flow in scenic spots is very important to the traffic management and safety of scenic spots. This study aims to predict the passenger flow of a scenic spot based on the passenger flow of the bus and subway stations around the scenic spots. We propose a passenger flow prediction model based on graph convolutional network–recurrent neural network (GCN–RNN). First, a “graph” is constructed according to the geographical relationship between the scenic spot and the surrounding bus and subway stations. Then, characteristics of surrounding areas of bus and subway stations are constructed based on the crowd behavior analysis, and these are then used as the node-information of the “graph”. Last, the GCN–RNN model is used to extract the temporal and spatial characteristics of the passenger flow data of the scenic spot to realize the prediction. The experimental results show that the proposed model is effective in passenger flow prediction in scenic spots.

Keywords: passenger flow prediction; deep learning; graph neural network; recurrent neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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