Paper Info
Title
Dyna-PTM: OD-enhanced GCN for Metro Passenger Flow Prediction
Abstract
Metro transit is an important part of the public transportation infrastructure and provides convenience for people's daily travel. Due to the limitation of capacity, under certain conditions, such as peak hours and severe weather, the traffic of metro stations will increase rapidly and cause congestion. Precise prediction of the passenger flow guarantees the metro's stable operation and passengers' safety. Previous to our study, several models based on spatial-temporal graph convolutional networks have been designed to handle this problem. Still, most of them have not considered the Original-Destination (OD) information adequately. Some only used the metro traffic network as a station adjacency matrix to describe stations' correlation without OD information. Others treated the OD information as a static adjacency matrix. However, the matrix is actually changing over time. This paper presents a novel method that converts the time-varying OD information into dynamic probability transition matrixes to effectively extract the dynamic correlation of stations in OD information into dynamic probability transition matrixes (Dyna-PTM). Dyna-PTM is a supplement adjacency matrix in the spatial-temporal graph convolutional network to describe stations' hidden and dynamic correlation. We verify Dyna-PTM using real metro datasets collected from two megacities in China - Chongqing, and Hangzhou. Experimental results demonstrate the superior performance of our method.
Year
DOI
Venue
2021
10.1109/IJCNN52387.2021.9534153
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Keywords
DocType
ISSN
Spatial-Temporal Correlation, Graph Convolutional Networks, Origin-Destination Matrix, Metro Passenger Flow Prediction
Conference
2161-4393
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Congjie He100.34
Haowei Wang200.34
Xinrui Jiang364.88
Meng Ma48212.29
Ping Wang500.68