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Monitoring Multimodal Travel Environment Using Automated Fare Collection Data: Data Processing and Reliability Analysis

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Abstract

Monitoring transit system “health” by extracting and tracking such quantities as travel time, transfer time, number of passengers, etc., is critical to the benefit of travelers, planners and operators within a transit system. Most of the data typically available to and useful for analysts are generated by tracking vehicles instead of individual passengers/travelers—these data are useful, albeit within certain limits. This paper presents methods for obtaining system-level transit information from a new type of data—that coming from an Automated Fare Collection (AFC) system,—which provides hour-to-hour, day-to-day transit information, such as the value and reliability in both travel time and traveler count, and the location of congested road clusters in a city. The AFC data of public transit system in Seoul, South Korea is used as an example to illustrate the proposed data extraction methods and analysis. This paper is structured and detailed so as to provide both methodological and practical guidance for researchers and data-handling analysts.

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Acknowledgements

This work was, in part, supported by the National Science Foundation Award 1636602 and Transportation Informatics University Transportation Center. The authors are grateful for their generous support.

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Authors and Affiliations

  1. University at Buffalo, The State University of New York, Buffalo, USA

    Laiyun Wu, Jee Eun Kang & Alexander Nikolaev

  2. Yeungnam University, Gyeongsan, Korea

    Younshik Chung

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  1. Laiyun Wu
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  2. Jee Eun Kang
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  3. Younshik Chung
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  4. Alexander Nikolaev
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Correspondence to Jee Eun Kang.

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Wu, L., Kang, J.E., Chung, Y. et al. Monitoring Multimodal Travel Environment Using Automated Fare Collection Data: Data Processing and Reliability Analysis. J. Big Data Anal. Transp. 1, 123–146 (2019). https://doi.org/10.1007/s42421-019-00012-w

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  • DOI: https://doi.org/10.1007/s42421-019-00012-w

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