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2024 Vol.42, Issue 6 Preview Page

Article

Development of Multi-Dimensional Mobility Indicators (MDMI) for Agent-based Travel Pattern Classification

객체 단위 통행 유형 구분을 위한 다차원 모빌리티 지표(MDMI) 개발

Minsu WON1
,
Jiyun SHIM2
,
Soojeong CHOI3
,
Jae Hyun LEE4*
원 민수1
,
심 지윤2
,
최 수정3
,
이 재현4*
1Associate Research Fellow, Department of Transport Big Data, Korea Transport Institute, Sejong 30147, Korea
2Researcher, Department of Transport Technology Research, Korea Transport Institute, Sejong 30147, Korea
3Graduate Student, Department of Geography, Kyungpook National University, Daegu 41566, Korea
4Assistant Professor, Department of Geography, Kyungpook National University, Daegu 41566, Korea
1한국교통연구원 교통빅데이터연구본부 부연구위원
2한국교통연구원 교통기술연구본부 연구원
3경북대학교 지리학과 석사과정
4경북대학교 지리학과 조교수
*Corresponding Author
31 December 2024. pp. 674-693
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Abstract

Each person has unique travel and activity patterns, and understanding these characteristics is crucial for analyzing diverse mobility behaviors within urban environments. This study proposes a new concept, the Multi-Dimensional Mobility Indicators (MDMI), to explore and classify individual mobility patterns at the agent level. First, various mobility indicators, such as travel time, travel distance, and number of trips, as well as individual-level indicators like residence time by activity space, complexity, and activity ellipse characteristics, are used to capture these patterns. Then, The MDMI classifies sixteen types of mobility patterns based on a combination of four criteria: 1) activity space and travel distance, 2) the ratio of in-home to out-of-home activities, 3) the complexity and roundness of the activity ellipse, and 4) time spent at work. Afterward, we analyze the relationship between the MDMI of each person and the factors affecting them, such as socioeconomic characteristics, region, etc. The analysis results show that various characteristics (i.e., age, gender, income, and primary means of transportation) significantly affect individual mobility patterns and their MDMI. For example, having a driver's license is associated with higher travel distances but did not engage in a greater number of activities. Moreover, residents of the Seoul Metropolitan Area (Seoul, Incheon, Gyeonggi) were spending a significant amount of time on commuting. These findings provide valuable insights into individual mobility patterns and offer guidance for improving transportation policies.

Keywords
individual travel survey
location-based data
mobility classification
mobility indicator
travel behavior analysis

시공간 단위의 통행 특성과 이동 형상은 개인에 따라 독특한 특성을 나타낸다. 본 연구에서는 이러한 모빌리티 특성을 몇 가지의 경향으로 유형화할 수 있음을 제안하였다. 유형화에 활용된 모빌리티 지표는 통행시간, 통행거리, 통행횟수 등의 기본적인 지표뿐만 아니라 위치기반 정보로 산출 가능한 미시적인 지표, 즉 활동장소별 체류시간, 모빌리티 복잡성, 활동 타원체 특성 지표 등을 포함하였다. 이를 활용하여 모빌리티 패턴을 16개 유형으로 구분하는 다차원 모빌리티 지표(Multi-Dimensional Mobility Indicators; MDMI)를 정의하였다. 유형화의 기준이 되는 요인은 4가지로 활동공간과 통행거리, 집 안과 집 밖 활동 비중, 복잡성과 활동 타원체의 원형성, 직장에서 보내는 시간으로 나타났다. 이후 MDMI 지표와 사회경제적 특성 및 지역적 특성 간 연관관계를 규명하였으며, MDMI 지표별, 유형별로 나타나는 독특한 특성을 확인하였다. 분석 결과, 연령, 성별, 소득수준, 주 이용 교통수단 등 다양한 사회경제적 특성이 개인의 모빌리티 유형에 유의미한 영향을 미치는 것으로 나타났다. 예를 들어, 운전면허 소지자는 미소지자보다 통행 거리가 길지만, 실내형, 단순장종(長縱)형의 비율이 높아 다양한 활동을 수행하지는 않는 것으로 나타났다. 또한 수도권 3개 시도(서울, 인천, 경기)는 장거리형, 실내형, 장시간 근무형의 비율이 높아 통근통행에 많은 시간을 할애하는 경향을 확인할 수 있었다. 본 연구는 개인의 모빌리티 패턴을 다차원적으로 이해하여, 기존 모빌리티 분석의 가능성을 확장하는 데 참고할 수 있을 것이다.

키워드
개인통행실태조사
위치기반 정보
모빌리티 유형화
모빌리티 지표
통행행태분석
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 42
  • No :6
  • Pages :674-693
  • Received Date : 2024-08-12
  • Revised Date : 2024-09-25
  • Accepted Date : 2024-11-12
  • DOI :https://doi.org/10.7470/jkst.2024.42.6.674
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