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http://dx.doi.org/10.22640/lxsiri.2022.52.2.153

Understanding elderly's travel pattern based on individual trip trajectory using smart card data  

Lee, Ju-Yoon (이화여자대학교 일반대학원 사회과교육학과 지리학 전공)
Kang, Young-Ok (이화여자대학교 사회과교육과 및 빅데이터 분석학 협동과정)
Publication Information
Journal of Cadastre & Land InformatiX / v.52, no.2, 2022 , pp. 153-169 More about this Journal
Abstract
With the extension of the average life span and the rapid aging of the population, defining elderly population as a single group is difficult as the physical, economic and social conditions of individual have become different. Therefore, policies that take into account the characteristics of each group are required. The purpose of this study is to classify individual travel types and to analyze the characteristics of each travel type, based on individual public transportation trajectory data as known as smart card data. Among the four classified types, the long-distance low-frequency stay type and the short-range medium-frequency mobile type show external activity traffic characteristics for retirement leisure, while the long-distance high-frequency stay type and the long-distance high-frequency mobile group include regular commuting. Traffic variability and residence areas of stay were identified in terms of each classified travel type. The results of this study provide the important suggestions for establishing a transportation policy that takes into account the characteristics of each type of elderly population in Seoul.
Keywords
Smart Card; Public Transport; Elderly Travel; Travel Pattern; Trajectory;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Go SW, Lee SI. 2017. A Study on Impact of Characteristics of Destination Districts of Seoul on Non-commuting Travel of Elderly Population by Age Groups, The Korean Regional Development Association. 29(1):79-97
2 Kim CG, Lee JH. 2015. A study on the Housing Choice of the Elderly according to the Financial Retirement Planning of Pre-seniors, Journal of Cadastre & Land InformatiX Corporation. 45(2):175-189.
3 Noh SH, Yang EJ. 2011. An analysis of the major travel patterns of the elderly in Seoul Metropolitan Area and their attitudes towards the free ride policy for subway use for elderly, The Geographical Journal of Korea. 45(4):545-557.
4 Yang HJ, Kim GH, Nam HW, Jun CM. 2018. An Individual Trip Dynamic Visualization method using Smart card Data, Journal of the Korean Society for Geospatial Information Science. 26(2):3-10   DOI
5 Lee JY, Kim HD, Kang YO. 2020. Analysis of Elderly Population's Staying Places in Seoul using Public Transportation Card Data, Journal of Cadastre & Land InformatiX Corporation. 50(1):231-245.
6 Lee HJ, Ha JH, Lee SJ. 2017. An Analysis on the Equity of Public Transit Service using Smart Card Data in Seoul, Korea - Focused on the Mobility of the Disadvantaged Population Groups -, Korean Regional Science Association. 33(3):101-113.
7 Jeon IW, Lee MH, Jun CH. 2019. Estimating the Trip Purposes of Public Transport Passengers Using Smartcard Data, Journal of the Korean Association of Geographic Information Studies. 22(1):28-38.   DOI
8 Jo JS, Park JY, Choi BN. 2010. A Study of the Factors Associated with Travel Patterns of the Elderly. The Korea Transport Institute.
9 Choo SH, Song JI, Kwon BS. 2011. Exploring Key Factors Influencing Travel of the Elderly: A Case of Seoul Metropolitan Area, Korea Planning Association. 46(2):235-250
10 Tak HJ, Kim YD, Kim HY, Song SH. 2019. An Analysis of Traffic Rules for Public Transportation Users Using Transportation Card Data: Focusing on Jeju Area. The Korea Institute of Intelligent Transport Systems. 218-226
11 Statistics Korea (2021). 2022 Elderly Statistics.
12 Korea Institute for Health and Social Arrairs. 2017. Health Status and Integrated Care for Elderly People: Focused on Healthcare.
13 Han SK, Lee HY. 2015. Characteristics of the Time-based Public Transportation Travel Flows and the Pull Factors of Travel Destinations of the Elderly in the Seoul Metropolitan Area. Seoul Studies. 16(2): 183-201.   DOI
14 Hong HH, Lee GJ, 2014. Analyzing Spatial Pattern by moving Factors of out-migration people Related moving to the Provinces of Capital Region Firms, Journal of Cadastre & Land InformatiX Corporation. 44(2):155-175.
15 Agard, B., Morency, C. and Trepanier, M., 2006. Mining public transport user behaviour from smart carddata, In: 12th IFAC Symposium on Information Control Problems in Manufacturing - INCOM 2006, Saint-Etienne,France. May 17-19.
16 Alsnih, R., and Hensher, D. A. 2003. The mobility and accessibility expectations of seniors in an aging population, Transportation Research Part A: Policy and Practice. 37(10):903-916.   DOI
17 Ester, M., Kriegel, H. P., Sander, J., and Xu, X. 1996, August. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd. 96(34):226-231.
18 Calinski, T. and Harabasz, J. 1974. A dendrite method for cluster analysis, Communications in Statistics-theory and Methods. 3(1):1-27.   DOI
19 Collia, D. V., Sharp, J., and Giesbrecht, L. 2003. The 2001 national household travel survey: A look into the travel patterns of older Americans, Journal of safety research. 34(4):461-470.   DOI
20 Duda, R. O. and Hart, P. E. 1973. Pattern Classification and Scene Analysis. John Wiley & Sons, New York.
21 Hubert, L. J. and Levin, J. R. 1976. A General Statistical Framework for Assessing Categorical Clustering in Free Recall, Psychological Bulletin. 83(6): 1072-1080.   DOI
22 Kim, S. 2003. Analysis of elderly mobility by structural equation modeling, Transportation research record. 1854(1): 81-89.   DOI
23 Kim, S. and Ulfarsson, G. F. 2004. Travel mode choice of the elderly: effects of personal, household, neighborhood, and trip characteristics, Transportation Research Record. 1894(1):117-126.   DOI
24 Kusakabe, T. and Asakura, Y. 2014. Behaviour data mining of transit smart card data: A data fusion approach, Transportation Research Part C: Emerging Technologies. 46:179-191.   DOI
25 Lloyd, S. 1982. Least squares quantization in PCM, IEEE transactions on information theory. 28(2):129-137.   DOI
26 Mohamed, K., Come, E., Baro, J., and Oukhellou, L. 2014. Understanding passenger patterns in public transit through smart card and socioeconomic data. UrbComp,(Seattle, WA, USA).
27 Tao, S., Rohde, D. and Corcoran, J. 2014. Examining the spatial-temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap, Journal of Transport Geography. 41:21-36.   DOI