Print Email Facebook Twitter Analysis of network-wide transit passenger flows based on principal component analysis Title Analysis of network-wide transit passenger flows based on principal component analysis Author Luo, D. (TU Delft Transport and Planning) Cats, O. (TU Delft Transport and Planning) van Lint, J.W.C. (TU Delft Transport and Planning) Date 2017-08-08 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. Subject multivariate passenger flowsprincipal component analysissmart card dataTransit system To reference this document use: http://resolver.tudelft.nl/uuid:981f6baf-6d58-4936-bf95-377bf0868d66 DOI https://doi.org/10.1109/MTITS.2017.8005611 Publisher Institute of Electrical and Electronics Engineers (IEEE) ISBN 9781509064847 Source 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings Event 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017, 2017-06-26 → 2017-06-28, Naples, Italy Part of collection Institutional Repository Document type conference paper Rights © 2017 D. Luo, O. Cats, J.W.C. van Lint Files PDF MT_ITS_2017_final.pdf 1.84 MB Close viewer /islandora/object/uuid:981f6baf-6d58-4936-bf95-377bf0868d66/datastream/OBJ/view