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.