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Activity-based TOD typology for seoul transit station areas using smart-card data

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  • Shin, Yonggeun
  • Kim, Dong-Kyu
  • Kim, Eui-Jin

Abstract

Transit-oriented development (TOD) is a planning strategy to encourage the use of public transportation by clustering urban development centering on transit stations with dense and diverse land uses. TOD typology is important information for the urban planner to monitor and improve the TOD. However, many studies have focused only on static features such as land use and population density around the transit station. Such analysis does not reflect dynamic features based on human mobility patterns. This study focuses on a series of processes that evaluate the activity-based TOD typology of the transit station based on smart-card data. To consider the context of multimodal transit system, transit station areas consisting of one subway station and multiple bus stops are created and clustered according to the proportion of the identified activities. Passengers' trip-chain with missing activities are clustered by applying a hidden Markov model (HMM), and the activities are identified through land use and temporal patterns. Interpretation of each cluster is made through spatio-temporal patterns. The results provide six types of identified activities that are well aligned with general expectations. The transit station areas in Seoul, which were clustered with six identified types, describe the urban spatial structure, which is spatially distributed around a business district in order of mixed and residential zones. Also, the same category of clusters shows different functions depending on temporal patterns, indicating the need for consideration of dynamic features. Our approach provides insights into transit station changes throughout the day that has been overlooked in previous studies by considering the passenger's sequence of activity, start, and duration.

Suggested Citation

  • Shin, Yonggeun & Kim, Dong-Kyu & Kim, Eui-Jin, 2022. "Activity-based TOD typology for seoul transit station areas using smart-card data," Journal of Transport Geography, Elsevier, vol. 105(C).
  • Handle: RePEc:eee:jotrge:v:105:y:2022:i:c:s096669232200182x
    DOI: 10.1016/j.jtrangeo.2022.103459
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    References listed on IDEAS

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    1. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    2. Cervero, Robert & Day, Jennifer, 2008. "Suburbanization and transit-oriented development in China," Transport Policy, Elsevier, vol. 15(5), pages 315-323, September.
    3. Allahviranloo, Mahdieh & Recker, Will, 2013. "Daily activity pattern recognition by using support vector machines with multiple classes," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 16-43.
    4. Ibraeva, Anna & Correia, Gonçalo Homem de Almeida & Silva, Cecília & Antunes, António Pais, 2020. "Transit-oriented development: A review of research achievements and challenges," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 110-130.
    5. Higgins, Christopher D. & Kanaroglou, Pavlos S., 2016. "A latent class method for classifying and evaluating the performance of station area transit-oriented development in the Toronto region," Journal of Transport Geography, Elsevier, vol. 52(C), pages 61-72.
    6. Kamruzzaman, Md. & Baker, Douglas & Washington, Simon & Turrell, Gavin, 2014. "Advance transit oriented development typology: case study in Brisbane, Australia," Journal of Transport Geography, Elsevier, vol. 34(C), pages 54-70.
    7. Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
    8. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    9. Lyu, Guowei & Bertolini, Luca & Pfeffer, Karin, 2016. "Developing a TOD typology for Beijing metro station areas," Journal of Transport Geography, Elsevier, vol. 55(C), pages 40-50.
    10. Su, Shiliang & Zhang, Hui & Wang, Miao & Weng, Min & Kang, Mengjun, 2021. "Transit-oriented development (TOD) typologies around metro station areas in urban China: A comparative analysis of five typical megacities for planning implications," Journal of Transport Geography, Elsevier, vol. 90(C).
    11. Liu, Yunzhe & Singleton, Alex & Arribas-Bel, Daniel, 2020. "Considering context and dynamics: A classification of transit-orientated development for New York City," Journal of Transport Geography, Elsevier, vol. 85(C).
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