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대규모 동적 O/D 생성을 위한 추정 방법론 연구: 첨두 출근통행을 기준으로

A Methodology for Estimating Large Scale Dynamic O/D of Commuter Working Trip

  • 한혁 (명지대학교 교통공학과) ;
  • 홍기만 (명지대학교 교통공학과) ;
  • 김태균 (명지대학교 교통공학과) ;
  • 황준문 (경기연구원 휴먼교통연구실) ;
  • 홍영석 (명지대학교 산업기술연구소) ;
  • 조중래 (명지대학교 교통공학과)
  • HAN, He (Department of Transportation Engineering, Myongji University) ;
  • HONG, Kiman (Department of Transportation Engineering, Myongji University) ;
  • KIM, Taegyun (Department of Transportation Engineering, Myongji University) ;
  • WHANG, Junmun (Office of Transportation Policy Research, Gyeonggi Research Institute) ;
  • HONG, Young Suk (Industrial Technology, Myongji University) ;
  • CHO, Joong Rae (Department of Transportation Engineering, Myongji University)
  • 투고 : 2018.03.09
  • 심사 : 2018.05.15
  • 발행 : 2018.06.30

초록

본 연구는 통행자의 통행패턴이 도착지의 토지이용패턴에 따라 변화하는 특징을 반영하여 대규모 동적 O/D를 구축하는 방법을 제안하였다. 기존 동적 O/D 추정 방법 관련 연구들을 살펴보면 자료수집의 어려움으로 소규모 지역에 국한하여 적용 가능하거나, 고속도로망 혹은 대중교통망 등 특정 교통망에 제한하여 O/D를 구축하는 등 한계가 존재한다. 이에 본 연구에서는 빅데이터 시대에 발맞추어 쉽게 수집, 이용이 가능한 교통 관련 자료들을 기반으로 분석 지역의 범위 제약 없이 동적 O/D를 추정하는 기법을 제시하였다. 군집 분석(Clustering Analysis) 기법을 이용하여 군집별 도착시간 기준 통행분포 비율로 출발시간 통행분포 비율을 산정하고 첨두 출발시간 분포함수를 추정하였다. 추정된 분포함수를 조사자료에 적용하여 비교 검증을 진행해본 결과 통계적으로 유의하게 나타났다.

This study suggests a method to construct large scale dynamic O/D reflecting the characteristic that the passengers' travel patterns change according to the land use patterns of the destination. There are limitations in the existing research about dynamic O/D estimation method, such as the difficulty of collecting data, which can be applied only to a small area, or limiting to a specific transportation network such as highway networks or public transportation networks. In this paper, we propose a method to estimate dynamic O/D without limitation of analysis area based on transportation resources that can be easily collected and used according to the big data era. Clustering analysis was used to calculate the departure time trip distribution ratio based on arrival time and departure time trip distribution function was estimated by each cluster. As a result of the comparison test with the survey data, the estimated distribution function was statistically significant.

키워드

참고문헌

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