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2025 Vol.43, Issue 3

Article

Identification of Latent Travel Pattern in Seoul Using Mobile Phone data

서울시 생활이동데이터를 이용한 잠재적 통행유형 분류

Sangjin HAN1
,
Keonwoong SHIN2
,
Wonbang SONG3*
한 상진1
,
신 건웅2
,
송 원방3*
1Associate Professor, Graduate School of Environmental Studies Transport, Seoul National University, Seoul 05220, Korea
2Data Analyst, MIMlab, Seoul 06744, Korea
3Master Course, Graduate School of Environmental Studies Transport, Seoul National University, Seoul 05220, Korea
1서울대학교 환경대학원 부교수
2(주)밈랩 사원
3서울대학교 환경대학원 석사과정
*Corresponding Author
30 June 2025. pp. 253-264
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Abstract

The aim of this paper is to classify characteristic of trips with high homogeneity using living-population data based on mobile signal data. 24,697,645 samples were classified by latent class analysis, a descriptive statistical model, and the model showed good fit and described characteristics of the classes well when classified into five classes. The classified classes are as follows: commuting trip, elderly trip, going home trip, leisure trip, and business trip. Among the different groups, it is noteworthy that the share of elderly trip is 23.3%, making it the second most important trip type after commuting trips (24.9%). This shows that the elderly are more socially active than in the past, as the number of people aged 65 and over has increased significantly in recent years. Therefore, tailored transportation policies for older populations are needed in addition to the free subway rides, and to do so, closer analysis of the travel characteristics of the aged people should be the first step.

Keywords
homogeniety
latent class analysis
seoul living population data
the elderly
trips

본 연구는 모바일 통신 데이터를 기반으로 추출한 생활이동데이터를 이용하여 다양한 통행유형들 중 동질성이 높은 특징적인 통행유형이 어떤 것인지 파악하고자 시도되었다. 이를 위해 24,697,645건의 샘플을 설명적 통계모형인 잠재계층분석으로 유형을 분류하였다. 그 결과 5개 계층으로 분류하는 것이 모형의 적합도도 높고 계층별 특징을 잘 설명할 수 있는 것으로 나타났다. 5개 계층은 통근 통행, 고령자 통행, 귀가 통행, 여가 통행, 업무통행 순으로 나타났다. 가장 두드러진 특징은 고령자 통행의 비중이 23.3%로 통근 통행(24.9%) 다음으로 중요한 통행유형으로 분류된 점이다. 이는 최근 65세 이상 인구가 크게 늘어나면서 고령자의 사회적 활동이 과거와 크게 다르다는 점을 잘 보여준다. 이는 향후 교통정책에서 고령자 맞춤형 정책이 강조될 필요가 있음을 시사한다.

키워드
동질성
잠재계층분석
서울생활이동데이터
고령자
통행
References
1

Alexander L., Jiang S., Murga M., González M. C. (2015), Origin-destination trips by purpose and time of day inferred from mobile phone data, Transportation Research Part C (58), 240-250.

10.1016/j.trc.2015.02.018
2

Bin M., Chung E., Park H. (2012), Study on commuting travel time divided by life cycle: Gyeonggi-do case, J. Korean Soc. Transp., 30(3), Korean Society of Transportation, 71-82.

10.7470/jkst.2012.30.3.071
3

Choo S., Lee H., Shin H. (2013), Analyzing changes in travel behavior of the elderly using travel dairy survey dta in Seoul metropolitan area, Land Research, 31-45.

10.15793/kspr.2013.76..003
4

Davis A. W., McBride E. C., Goulias K. G. (2018), A latent class pattern recognition and data quality assessment of non-commute long-distance travel in California, Transportation research record, 2672(42), 71-80.

10.1177/0361198118787362
5

Delbosc A., Naznin F. (2019), Future life course and mobility: A latent class analysis of young adults in Victoria, Australia, Transport Policy, 77, 104-116.

10.1016/j.tranpol.2019.03.005
6

Ghosh S. K. (1991), Econometrics: Theory and Applications, Prentice Hall.

7

Han S. J., Lim J. K., Uhm K. J., Kim H. E. (2020), Better Driving License Management and Mobility Services for the Elderly, Korea Transport Institute Basic Research Report, 1-168.

8

Kim J. Y., Jang, D. I., Kim D. H., Choo S. H., Kim K. Y. (2021), Analysis of National Social and Economic Activities Based on Mobility Big Data, Korea Transport Institute, Basic Research Report, 1-192.

9

Kim K. S., Kwon K. S. (2020), The Impacts of Urban Built Environments on Travel Behavior of the Elderly: The Case of Seoul Metropolitan Area, Journal of the Korean Society of Urban Geography, 23(2), 109-124.

10.21189/JKUGS.23.2.8
10

Kim S. H., Choo S. (2019), Developing a Latent Class Model Considering Heterogeneity in Mode Choice Behavior: A Case of Commuters in Seoul, 한국ITS학회 논문지, 18(2), 44-57.

10.12815/kits.2019.18.2.44
11

Kroesen M. (2019), Is active travel part of a healthy lifestyle? Results from a latent class analysis, Journal of Transport & Health, 12, 42-49.

10.1016/j.jth.2018.11.006
12

Lee J. G., Lee E. H. (2022), Exploring the Change of Population Flows in an Urban Area during the COVID-19 Pandemic - Focused on the Commuting Pattern based on Seoul Daily Life Movement Data, Journal of the Korean Urban Geographical Society, 25(2), 15-32.

10.21189/JKUGS.25.2.2
13

Linzer D. A., Lewis J. B. (2011), poLCA: An R package for polytomous variable latent class analysis, Journal of statistical software, 42, 1-29.

10.18637/jss.v042.i10
14

Molin E., Mokhtarian P., Kroesen M. (2016), Multimodal travel groups and attitudes: A latent class cluster analysis of Dutch travelers, Transportation Research Part A: Policy and Practice, 83, 14-29.

10.1016/j.tra.2015.11.001
15

Nagin D. (2005), Group-based modeling of development, Harvard University Press.

10.4159/9780674041318PMC2538721
16

Oh B. R. (2014), A Study on travel characteristics and the establishment of criterion for the size of the neighborhood unit by using the data of household travel diary survey in Seoul, Journal of Seoul Urban Studies, 15(3), 1-18.

17

Ortuzar J., Willumsen L. G. (2008), Modelos de transporte (Vol. 1). Ed. Universidad de Cantabria.

18

Park J., Kim S., Lee S. (2022), Analysis of Determining Factors of Urban Vitality with Mobile Phone Location-Based Origin-Destination Bigdata by Travel Purpose: Using the PageRank Algorithm and SHAP Machine Learning, Journal of Korea Planning Association, 57(5), 72-89.

10.17208/jkpa.2022.10.57.5.72
19

Rafiq R., McNally M. G. (2021), Heterogeneity in activity-travel patterns of public transit users: An application of latent Class Analysis, Transportation Research Part A: Policy and Practice, 152, 1-18.

10.1016/j.tra.2021.07.011
20

Shirgaokar M., Dobbs B., Anderson L., Hussey E. (2020), Do rural older adults take fewer trips than their urban counterparts for lack of a ride? Journal of Transport Geography, 87, 102819.

10.1016/j.jtrangeo.2020.102819
21

Sinha P, Calfee C. S., Delucchi K. L. (2021) Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls, Crit Care Med., 1;49(1):e63-e79.

10.1097/CCM.000000000000471033165028PMC7746621
22

Statistics Korea (2022), 2022 Ederly Statistics.

23

Szeto W., Yang L., Wong R., Li Y., Wong S. (2017), Spatio-temporal travel characteristics of the elderly in an ageing society, Travel Behaviour and Society, 9, 10-20.

10.1016/j.tbs.2017.07.005
24

Yang Y., Xu Y., Rodriguez D. A., Michael Y., Zhang H. (2018), Active travel, public transportation use, and daily transport among older adults: The association of built environment, Journal of Transport & Health, 9, 288-298.

10.1016/j.jth.2018.01.012
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 43
  • No :3
  • Pages :253-264
  • Received Date : 2025-01-17
  • Revised Date : 2025-02-26
  • Accepted Date : 2025-03-13
  • DOI :https://doi.org/10.7470/jkst.2025.43.3.253
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