Analysis of network-wide transit passenger flows based on principal component analysis

Abstract

Transit networks are complex systems in which the passenger flow dynamics are difficult to capture and understand. While there is a growing ability to monitor and record travelers' behavior in the past decade, knowledge on network-wide passenger flows, which are essentially high-dimensional multivariate data, is still limited. This paper describes how Principal Component Analysis (PCA) can be leveraged to develop insight into such multivariate time series transformed from raw individual tapping records of smart card data. With a one-month data set of the Shenzhen metro system used in this study, it is shown that a great amount of variance contained in the original data can be effectively retained in lower-dimensional sub-spaces composed of top few Principal Components (PCs). Features of such low dimensionality, PCs and temporal stability of the flow structure are further examined in detail. The results and analysis provided in this paper make a contribution to the understanding of transit flow dynamics and can benefit multiple important applications for transit systems, such as passenger flow modeling and short-term prediction.