• Title/Summary/Keyword: Platform Congestion

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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;Choi, Jin-Kyung
    • Journal of the Korean Society for Railway
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    • v.18 no.3
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    • pp.270-277
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    • 2015
  • 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.

Development of the Train Dwell Time Model : Metering Strategy to Control Passenger Flows in the Congested Platform (승강장 혼잡관리를 위한 열차의 정차시간 예측모형)

  • KIM, Hyun;Lee, Seon-Ha;LIM, Guk-Hyun
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.16 no.3
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    • pp.15-27
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    • 2017
  • 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.

A Model for Analyzing Time-Varying Passengers' Crowdedness Degree of Subway Platforms Using Smart Card Data (스마트카드자료를 활용한 지하철 승강장 동적 혼잡도 분석모형)

  • Shin, Seongil;Lee, Sangjun;Lee, Changhun
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.18 no.5
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    • pp.49-63
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    • 2019
  • Crowdedness management at subway platforms is essential to improve services, including the prevention of train delays and ensuring passenger safety. Establishing effective crowdedness mitigation measures for platforms requires accurate estimation of the congestion level. There are temporal and spatial constraints since crowdedness on subway platforms is assessed at certain locations every 1-2 years by hand counting. However, smart cards generate real-time big data 24 hours a day and could be used in estimating congestion. This study proposes a model based on data from transit cards to estimate crowdedness dynamically. Crowdedness was defined as demand, which can be translated into passengers dynamically moving along a subway network. The trajectory of an individual passenger can be identified through this model. Passenger flow that concentrates or disperses at a platform is also calculated every minute. Lastly, the platform congestion level is estimated based on effective waiting areas for each platform structure.