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

  • 제22권1호
  • /
  • Pages.219-232
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  • 2023
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  • 1738-0774(pISSN)
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  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
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기종점 모빌리티 데이터 기반 클러스터링 기법을 활용한 지역 모빌리티의 공간적 특성 분석 연구

A Study on the Analysis of Spatial Characteristics with Respect to Regional Mobility Using Clustering Technique Based on Origin-Destination Mobility Data

  • 이동훈 (한국교통연구원 모빌리티전환연구본부) ;
  • 안용준 (대전세종연구원 세종연구실)
  • Donghoun Lee (Dept. of Mobility Transformation, Korea Transport Institute) ;
  • Yongjun Ahn (Daejeon-Sejong Research Institute)
  • 투고 : 2022.12.05
  • 심사 : 2023.01.06
  • 발행 : 2023.02.28
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초록

모빌리티 서비스는 구축 대상 지역의 특성과 여건에 따라 변화할 필요가 있다. 이를 위해서는 해당 지역의 통행행태를 기종점 자료에 반영하여 모빌리티 패턴 및 특성 분석이 요구된다. 그러나 종래의 경우 행정 구역 기반의 존 체계를 기반으로 집계된 기종점 자료를 이용함에 따라 공간적 동질성을 담보하기 어렵기 때문에 신규 모빌리티와 같은 특수 목적성을 보이는 수단에 대한 본연의 통행 특성 분석에 한계가 있다. 이에 본 연구는 기존 존 체계에서 벗어나 데이터 기반의 클러스터링 기법 적용을 통해 설정된 집계 방식을 도출하여 기종점 통행패턴에 대한 공간적 분석을 수행한다. 제안 방법은 대중교통버스 및 택시와 같은 종래의 교통수단 뿐만 아니라 도심형 수요응답형 버스와 같은 신규 모빌리티 서비스에 대한 기종점 데이터 본연의 특징 벡터들을 기반으로 클러스터링을 하여 유사 공간적 특성을 반영한 지역 모빌리티의 이용 특성 분석을 가능하게 한다.

Mobility services need to change according to the regional characteristics of the target service area. Accordingly, analysis of mobility patterns and characteristics based on Origin-Destination (OD) data that reflect travel behaviors in the target service area is required. However, since conventional methods construct the OD data obtained from the administrative district-based zone system, it is hard to ensure spatial homogeneity. Hence, there are limitations in analyzing the inherent travel patterns of each mobility service, particularly for new mobility service like Demand Responsive Transit (DRT). Unlike the conventional approach, this study applies a data-driven clustering technique to conduct spatial analyses on OD travel patterns of regional mobility services based on reconstructed OD data derived from re-aggregation for original OD distributions. Based on the reconstructed OD data that contains information on the inherent feature vectors of the original OD data, the proposed method enables analysis of the spatial characteristics of regional mobility services, including public transit bus, taxi and DRT.

키워드

과제정보

본 연구는 한국교통연구원에서 진행한 '2022 국가모빌리티 대전환 지원사업' 연구과제의 일환으로 수행되었습니다.

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