Development of an Algorithm for Estimating Subway Platform Congestion Using Public Transportation Card Data

대중교통카드 자료를 활용한 도시철도 승강장 혼잡도 추정 알고리즘 개발

  • Lee, Ho (Department of Railway Transport Research, The Korea Transport Institute) ;
  • Choi, Jin-Kyung (Department of Railway Transport Research, The Korea Transport Institute)
  • Received : 2015.04.20
  • Accepted : 2015.06.09
  • Published : 2015.06.30


In some sections of the Seoul Metropolitan Subway, severe congestion can be observed during rush hours and on specific days. The subway operators have been conducting regular surveys to measure the level of congestion on trains: the results are then used to make plans for congestion reduction. However, the survey has so far focused just on train' congestion and has been unable to determine non-recurring congestion due to special events. This study develops an algorithm to estimate the platform congestion rate by time using individual public transportation card data. The algorithm is evaluated by comparison of the estimated congestion rate and the ground truth data that are actually observed at non-transfer subway stations on Seoul subway line 2. The error rates are within ${pm}2%$ and the performance of the algorithm is fairly good. However, varying walking times from gates to platforms, which are applied to both non-peak periods and peak time periods, are needed to improve the algorithm.

수도권 도시철도 일부 구간에서는 이용객의 집중화에 따라 극심한 혼잡이 발생하고 있다. 도시철도 운영회사들은 정기 및 분기별 혼잡조사를 실시하여, 혼잡개선 대책을 수립하고 있다. 하지만, 이러한 조사는 열차혼잡도에 국한되어 있으며, 이벤트 발생에 따른 비반복 혼잡에 대한 조사가 어려운 실정이다. 본 연구에서는 교통카드자료를 활용한 시간대별 승강장 혼잡도를 추정하는 알고리즘을 개발하도록 한다. 알고리즘 검증을 위하여 2호선 잠실~신도림 구간의 비환승역을 대상으로 혼잡도 추정치와 실측치 값을 비교하였으며, 오차의 범위는 ${pm}2%$ 이내였다. 연구결과는 승강장 혼잡도를 시간대별로 상시 모니터링할 수 있으며, 장기적인 승강장 혼잡도분석을 통한 승강장 대기공간의 적정성 여부도 판단할 수 있을 것이다. 추후 연구에서는 본 연구에서 반영하지 못한 역별 게이트에서 승강장까지의 혼잡상황을 고려한 동적보행시간이 고려되어야 할 것이다.



Supported by : 국토교통부


  1. (Accessed 17 April 2015).
  2. (Accessed 17 April 2015).
  3. Korea Transport Database Center (2010) Korea Transport Survey and Database 2010, The Korea Transport Institute.
  4. Y.S. Lee (1997) Traffic Survey in The Seoul Metropolitan Subway Lines, The National Railroad Administration.
  5. S.I. Shin (2011) Congestion Index of Urban Rail Transit Using Public Transportation Card Data, Seoul Development Institute, Working Paper 2011-BR-04.
  6. J.K Eom, M.H Choi, D.S Kim, J. Lee, J.Y Song (2012) Evaluation of Metro Services based on Transit Smart Card Data(A Case Study of Incheon Line 1), Journal of the Korean Society for Railway, 15(1), pp. 80-87.
  7. J. Chan (2007) Rail Transit OD Matrix Estimation and Journey Time Reliability Metrics Using Automated Fare Data, The degree of Master of Science in Transportation, Massachusetts Institute of Technology.
  8. J.P.A. van den Heuvel, J.H. Hoogenraad (2014) Monitoring the Performance of the Pedestrian Transfer Function of Train Stations Using Automatic Fare Collection Data, Transportation Research Procedia, Volume 2, pp. 642-650.
  9. I. Gokasar, K. Simsek, K. Ozbay (2014) Using Big Data of Automated Fare Collection System for Analysis and Improvement of BRT in Istanbul, Transportation Research Board, TRB 2015 Annual meeting.
  10. (Accessed 26 March 2014).
  11. Metropolitan Railway Division (2013) Station and Accommodations of Design Principle in Subway, Ministry of Land, Infrastructure and Transport.

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