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승강장 혼잡관리를 위한 열차의 정차시간 예측모형

Development of the Train Dwell Time Model : Metering Strategy to Control Passenger Flows in the Congested Platform

  • KIM, Hyun (Dept. of Railway Transport Research, The Korea Transport Institute) ;
  • Lee, Seon-Ha (Dept. of Urban and Transportation Engineering, Univ. of Kongju) ;
  • LIM, Guk-Hyun (Dept. of Railway Transport Research, The Korea Transport Institute)
  • 투고 : 2017.03.23
  • 심사 : 2017.04.18
  • 발행 : 2017.06.30

초록

열차 정차시간 증가는 열차 서비스 빈도를 감소시켜 열차와 승강장의 혼잡이 발생하게 된다. 그러므로 열차 정차시간(Train dwell time)에 관한 연구는 열차 운행 계획수립 관점에서 매우 중요하게 다루어 왔다. 본 논문은 계획된 정차시간을 준수할 수 있도록 승객의 유입을 관리하여 승강장 혼잡을 줄일 수 있는 전략들에 활용할 수 있는 실시간 열차 정차시간 예측모형을 개발하였다. 모형의 특징은 실시간으로 수집 가능한 승차인원, 하차인원, 그리고 열차의 중량 등 3가지 독립변수를 적용하였고, 모형의 설명력(${\bar{R^2}}=0.809$)이 대체적으로 정확한 결과를 보여주었다. 실시간 정차시간 모형은 열차가 계획된 정차시간을 준수하도록 승차 승객 수를 조정하는 게이트 미터링 전략에 활용될 수 있다.

In general, increasing train dwell time leads to increasing train service frequency, and it in turn contributes to increasing the congestion level of train and platform. Therefore, the studies on train dwell time have received growing attention in the perspective of scheduling train operation. This study develops a prediction model of train dwell time to enable train operators to mitigate platform congestion by metering passenger inflow at platform gate with respect to platform congestion levels in real-time. To estimate the prediction model, three types of independent variables were applied: number of passengers to get into train, number of passengers to get out of trains, and train weights, which are collectable in real-time. The explanatory power of the estimated model was 0.809, and all of the dependent variables were statistically significant at the 99%. As a result, this model can be available for the basis of on-time train service through platform gate metering, which is a strategy to manage passenger inflow at the platform.

키워드

참고문헌

  1. Daniel J. R. and Rotter N. G.(2009), Customer Behaviour Relative to Gap Between Platform and Train, New Jersey Institute of Technology.
  2. Hansen I. A. et al.(2010), "Online Train Delay Recognition and Running Time Prediction," 13th International IEEE Annual Conference on Intelligent Transportation Systems, pp.1783-1788.
  3. Harris N. G.(2006), "Train boarding and alighting rates at high passenger loads," Journal of advanced Transportation, vol. 40, no. 3, pp.249-263. https://doi.org/10.1002/atr.5670400302
  4. Jong J. and Chang S.(2005), "Algorithms for generating train speed profiles," Journal of the Eastern Asia Society for Transportation Studies, vol. 6, pp.356-371.
  5. Kecman P. and Goverde R.(2013), "An online railway traffic prediction model," Proceedings of the 5th International Seminar on Railway Operations Modelling and Analysis, Copenhagen.
  6. Lam W. H. et al.(1998), "A study of train dwelling time at the Hong Kong mass transit railway system," Journal of advanced Transportation, vol. 32, pp.285-295. https://doi.org/10.1002/atr.5670320303
  7. Li D., Goverde R. et al.(2014), "Train Dwell Time Distributions at Short Stop Stations," Proceedings of 17th International IEEE Conference on Intelligent Transportation Systems, October 8-11, Qingdao, China.
  8. Lin T.-M. and Wilson N. H. M.(1992), Dwell Time Relationships for Light Rail Systems, Transportation Research Record, pp.287-295.
  9. Oh S. -M.(2005), "An Analysis of the Passenger Flow Time in the Congested Subway Stations," Conference of the Korean Society for Railway, pp.42-49.
  10. Parkinson T. and Fisher I.(1996), TCRP Report 13: Rail Transit Capacity, Transportation Research Board, National Research Council, Washington, D.C.
  11. Puong A.(2000), Dwell time model and analysis for the MBTA red lin, Massachusetts Institute of Technology Research Memo.
  12. Shon(2013), "Optimizing Train-Stop Positions Along a Platform to Distribute the Passenger Load More Evenly Across Individual Cars," IEEE Transactions on intelligent transportation systems, vol. 14, no. 2. pp.994-1002. https://doi.org/10.1109/TITS.2013.2252166
  13. Washington Metropolitan Area Transit Authority(2005), Passenger Flow and Train Dwell Time.
  14. Weston J. G.(1989), Train service model - technical guide. London Underground operational research note, 89/18.
  15. Wiggenraad P.(2001), Alighting and boarding times of passengers at Dutch railway stations, TRAIL Research School, Delft.
  16. Wirasinghe S C. and Szplett D.(1984), "An Investigation of Passenger Interchange and Train Standing Time at LRT Stations," Journal of Advanced Transportation, vol. 18, no. 1, pp.13-24. https://doi.org/10.1002/atr.5670180103