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Estimation of Mass Rapid Transit Passenger's Train Choice Using a Mixture Distribution Analysis

통행시간 기반 혼합분포모형 분석을 통한 도시철도 승객의 급행 탑승 여부 추정 연구

  • Jang, Jinwon (Dept. of Transportation Engineering & Dept. of Smart Cities) ;
  • Yoon, Hosang (Dept. of Transportation Engineering & Dept. of Urban Big Data Convergence) ;
  • Park, Dongjoo (Dept. of Transportation Engineering & Dept. of Urban Big Data Convergence)
  • 장진원 (서울시립대학교 교통공학과 & 스마트시티학과) ;
  • 윤호상 (서울시립대학교 교통공학과 & 도시빅데이터융합학과) ;
  • 박동주 (서울시립대학교 교통공학과 & 도시빅데이터융합학과)
  • Received : 2021.07.21
  • Accepted : 2021.09.08
  • Published : 2021.10.31

Abstract

Identifying the exact train and the type of train boarded by passengers is practically cumbersome. Previous studies identified the trains boarded by each passenger by matching the Automated Fare Collection (AFC) data and the train schedule diagram. However, this approach has been shown to be inefficient as the exact train boarded by a considerable number of passengers cannot be accurately determined. In this study, we demonstrate that the AFC data - diagram matching technique could not estimate 28% of the train type selected by passengers using the Seoul Metro line no.9. To obtain more accurate results, this paper developed a two-step method for estimating the train type boarded by passengers by applying the AFC data - diagram matching method followed by a mixture distribution analysis. As a result of the analysis, we derived reasonable express train use/non-use passenger classification points based on 298 origin-destination pairs that satisfied the verification criteria of this study.

대부분의 도시철도 시스템은 승객의 탑승열차 및 탑승열차종을 정확히 알 수 없다. 다수의 선행연구에서는 교통카드데이터와 열차시각표를 매칭하여 탑승열차를 추정하였으나, 추정이 불가능한 승객 또한 다수 존재한다. 본 연구의 9호선 사례분석 결과 교통카드데이터-열차시각표 매칭만으로는 약 28% 승객의 탑승열차종을 추정할 수 없음을 확인할 수 있었다. 이에 교통카드데이터-열차시각표 매칭과 본 연구에서 정의한 통행시간 기반 혼합확률분포분석을 순차적으로 적용하여 급행운영 도시철도노선 승객의 탑승열차종을 추정하는 방법을 개발하였다. 분석 결과, 298개 OD pair에서 본 연구의 검증 기준을 만족하는 합리적인 급행이용/비이용 승객 분류기준점을 도출할 수 있었다.

Keywords

Acknowledgement

본 논문은 2021년 한국철도학회 춘계학술대회에서 발표되었던 논문을 수정·보완하여 작성하였습니다.

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