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Urban Sci. 2018, 2(3), 65; https://doi.org/10.3390/urbansci2030065

Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro

Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA 22030, USA
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Received: 26 June 2018 / Revised: 28 July 2018 / Accepted: 5 August 2018 / Published: 7 August 2018
(This article belongs to the Special Issue The Future of Urban Transportation and Mobility Systems)
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Abstract

The problem of traffic prediction is paramount in a plethora of applications, ranging from individual trip planning to urban planning. Existing work mainly focuses on traffic prediction on road networks. Yet, public transportation contributes a significant portion to overall human mobility and passenger volume. For example, the Washington, DC metro has on average 600,000 passengers on a weekday. In this work, we address the problem of modeling, classifying and predicting such passenger volume in public transportation systems. We study the case of the Washington, DC metro exploring fare card data, and specifically passenger in- and outflow at stations. To reduce dimensionality of the data, we apply principal component analysis to extract latent features for different stations and for different calendar days. Our unsupervised clustering results demonstrate that these latent features are highly discriminative. They allow us to derive different station types (residential, commercial, and mixed) and to effectively classify and identify the passenger flow of “unknown” stations. Finally, we also show that this classification can be applied to predict the passenger volume at stations. By learning latent features of stations for some time, we are able to predict the flow for the following hours. Extensive experimentation using a baseline neural network and two naïve periodicity approaches shows the considerable accuracy improvement when using the latent feature based approach. View Full-Text
Keywords: modeling; prediction; traffic; passenger volume; public transport; train station; time series modeling; prediction; traffic; passenger volume; public transport; train station; time series
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Truong, R.; Gkountouna, O.; Pfoser, D.; Züfle, A. Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro. Urban Sci. 2018, 2, 65.

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