Version 1
: Received: 5 March 2020 / Approved: 6 March 2020 / Online: 6 March 2020 (09:02:01 CET)
How to cite:
Shin, S.S.; Lee, S. An Analysis of Time-Varying Crowding on Subway Platforms using AFC Data in Seoul Metropolitan Subway Network. Preprints2020, 2020030109. https://doi.org/10.20944/preprints202003.0109.v1
Shin, S.S.; Lee, S. An Analysis of Time-Varying Crowding on Subway Platforms using AFC Data in Seoul Metropolitan Subway Network. Preprints 2020, 2020030109. https://doi.org/10.20944/preprints202003.0109.v1
Shin, S.S.; Lee, S. An Analysis of Time-Varying Crowding on Subway Platforms using AFC Data in Seoul Metropolitan Subway Network. Preprints2020, 2020030109. https://doi.org/10.20944/preprints202003.0109.v1
APA Style
Shin, S.S., & Lee, S. (2020). An Analysis of Time-Varying Crowding on Subway Platforms using AFC Data in Seoul Metropolitan Subway Network. Preprints. https://doi.org/10.20944/preprints202003.0109.v1
Chicago/Turabian Style
Shin, S.S. and Sangjun Lee. 2020 "An Analysis of Time-Varying Crowding on Subway Platforms using AFC Data in Seoul Metropolitan Subway Network" Preprints. https://doi.org/10.20944/preprints202003.0109.v1
Abstract
Management of crowding at subway platform is essential to improving services, preventing train delays and ensuring passenger safety. Establishing effective measures to mitigate crowding at platform requires accurate estimation of actual crowding levels. At present, there are temporal and spatial constraints since subway platform crowding is assessed only at certain locations, done every 1~2 years, and counting is performed manually Notwithstanding, data from smart cards is considered real-time big data that is generated 24 hours a day and thus, deemed appropriate basic data for estimating crowding. This study proposes the use of smart card data in creating a model that dynamically estimates crowding. It first defines crowding as demand, which can be translated into passengers dynamically moving along a subway network. In line with this, our model also identifies the travel trajectory of individual passengers, and is able to calculate passenger flow, which concentrates and disperses at the platform, every minute. Lastly, the level of platform crowding is estimated in a way that considers the effective waiting area of each platform structure.
Keywords
Automated Fare Collection (AFC); Smart Card; Crowding; Practical Waiting Area; Subway Station Platform; Time-Varying; Late-Night Peak
Subject
Computer Science and Mathematics, Information Systems
Copyright:
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.
