Next Article in Journal
PPGIS and Public Use in Protected Areas: A Case Study in the Ebro Delta Natural Park, Spain
Next Article in Special Issue
Spatial Keyword Query of Region-Of-Interest Based on the Distributed Representation of Point-Of-Interest
Previous Article in Journal
Shared Data Sources in the Geographical Domain—A Classification Schema and Corresponding Visualization Techniques
Previous Article in Special Issue
Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China
Open AccessArticle

Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

by 1,2, 1,2,*, 1,2, 1, 3 and 1,2
1
College of Information Science and Engineering, Ocean University of China, No. 238, Songling Road, Qingdao 266100, China
2
Pilot National Laboratory for Marine Science and Technology (Qingdao), No. 1, Wenhai Road, Qingdao 266237, China
3
Qingdao Transportation Public Service Center, No. 163, Shenzhen Road, Qingdao 266061, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(6), 243; https://doi.org/10.3390/ijgi8060243
Received: 7 May 2019 / Accepted: 27 May 2019 / Published: 28 May 2019
Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this paper, we propose a novel deep-learning-based approach, named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro), to collectively predict two types of passenger flow volumes—inflow and outflow—in each metro station of a city. Specifically, instead of representing metro stations by grids and employing conventional convolutional neural networks (CNNs) to capture spatiotemporal dependencies, STGCNNmetro transforms the city metro network to a graph and makes predictions using graph convolutional neural networks (GCNNs). First, we apply stereogram graph convolution operations to seamlessly capture the irregular spatiotemporal dependencies along the metro network. Second, a deep structure composed of GCNNs is constructed to capture the distant spatiotemporal dependencies at the citywide level. Finally, we integrate three temporal patterns (recent, daily, and weekly) and fuse the spatiotemporal dependencies captured from these patterns to form the final prediction values. The STGCNNmetro model is an end-to-end framework which can accept raw passenger flow-volume data, automatically capture the effective features of the citywide metro network, and output predictions. We test this model by predicting the short-term passenger flow volume in the citywide metro network of Shanghai, China. Experiments show that the STGCNNmetro model outperforms seven well-known baseline models (LSVR, PCA-kNN, NMF-kNN, Bayesian, MLR, M-CNN, and LSTM). We additionally explore the sensitivity of the model to its parameters and discuss the distribution of prediction errors. View Full-Text
Keywords: citywide metro network; graph convolution neural network; spatiotemporal flow-volume prediction; deep learning citywide metro network; graph convolution neural network; spatiotemporal flow-volume prediction; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Han, Y.; Wang, S.; Ren, Y.; Wang, C.; Gao, P.; Chen, G. Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks. ISPRS Int. J. Geo-Inf. 2019, 8, 243.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop