한국ITS학회 논문지 (The Journal of The Korea Institute of Intelligent Transport Systems)

  • 제18권6호
  • /
  • Pages.11-30
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  • 2019
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  • 1738-0774(pISSN)
  • /
  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
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의사결정 학습 모델 기반 교통카드 데이터 하차 정류장 추정 모델 연구

A Study of Estimating the Alighting Stop on the Decision Tree Learning Model Using Smart Card Data

  • 유봉석 (홍익대학교 도시공학) ;
  • 추상호 (홍익대학교 건설도시공학부)
  • 투고 : 2019.01.27
  • 심사 : 2019.12.20
  • 발행 : 2019.12.31
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초록

교통카드 데이터는 다양한 대중교통 통계 지표 산출, 정책 및 평가를 위한 자료로 활용되어 그 활용범위가 상당히 높다. 그러나 교통카드 데이터 내 주요 문제점은 하차 정류장에서 태그를 안 하고 하차하는 경우가 대부분으로 이는 교통카드 이용자의 불완전한 OD 통행 자료로 활용범위에 있어 한계가 있다. 본 연구는 의사결정 모델 기반 교통카드 데이터 하차 정류장 추정 방법을 적용한 결과 오차 범위 2개 정류장 이하에서 하차 정류장 추정 정확도는 89.7%으로 분석되었다. 이를 통하여 교통카드 데이터의 불완전성을 해소함으로써 다양한 대중교통 분석 및 평가 등에 대한 기초 자료로 활용 될 수 있을 것으로 판단된다.

Smartcards are used as the basic data for utilizing the various transportation policies and evaluations, etc. and provided the transportation basic statistics index. However, the main problem of the smartcard data is that the most of users do not take the alighting tag at the stop, so there is a limit to the scope of use for the total O-D trip data because incomplete O-D traffic data of transportation card users. In this study, a decision tree of learning model is estimated for the alighting stop of smartcard users. The model estimation accuracy in range less than 2 stops interval was 89.7% on average. By eliminating the incompleteness alighting stop of smartcard data through this model, it is expected to be used as the basic data for various transportation analyses and evaluations.

키워드

참고문헌

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피인용 문헌

  1. Estimating Destination of Bus Trips Considering Trip Type Characteristics vol.11, pp.21, 2019, https://doi.org/10.3390/app112110415