IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i24p10482-d462421.html
   My bibliography  Save this article

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

Author

Listed:
  • Younghoon Seo

    (Intelligent Transportation System Lab, Advanced Institute of Convergence Technology, Suwon 16229, Korea)

  • Donghyun Lim

    (Intelligent Transportation System Lab, Advanced Institute of Convergence Technology, Suwon 16229, Korea)

  • Woongbee Son

    (Gyeonggi Autonomous Driving Center, Advanced Institute of Convergence Technology, Seongnam 13449, Korea)

  • Yeongmin Kwon

    (Policy Research Team, Incheon International Airport Corporation, Incheon 22382, Korea)

  • Junghwa Kim

    (Department of Urban and Transportation Engineering, Kyonggi University, Suwon 16227, Korea)

  • Hyungjoo Kim

    (Intelligent Transportation System Lab, Advanced Institute of Convergence Technology, Suwon 16229, Korea)

Abstract

Mobility services facilitate various tasks related to transportation and passenger movements. Because of the Fourth Industrial Revolution, the importance of mobility services has been recognized by many countries. Thus, research is ongoing to provide more convenience to passengers and to obtain more efficient transportation systems. In the Republic of Korea, the officials of Gyeonggi Province are interested in providing an advanced mobility service to its residents; however, they still do not have any specific or detailed policies. This study aimed at deriving the key issues facing mobility services, especially in the case of Gyeonggi Province, by using a text mining technique and a clustering algorithm. First, a survey was taken by traffic and urban experts to collect reasonable plans for Gyeonggi-Province-type mobility service, and a morpheme analysis was then used for text mining. Second, the results reveal that the term frequency–inverse document frequency (TF-IDF) algorithm has better performance than frequency analysis. Third, the K-means application results in six clusters and six mobility service policy issues were determined by combining the words in each cluster. Finally, the methodology confirmed the validity and effectiveness of the proposed method by showing that the results reflect the current situation in the province.

Suggested Citation

  • Younghoon Seo & Donghyun Lim & Woongbee Son & Yeongmin Kwon & Junghwa Kim & Hyungjoo Kim, 2020. "Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10482-:d:462421
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/24/10482/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/24/10482/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Frändberg, Lotta & Vilhelmson, Bertil, 2011. "More or less travel: personal mobility trends in the Swedish population focusing gender and cohort," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1235-1244.
    2. Florian Dandl & Michael Hyland & Klaus Bogenberger & Hani S. Mahmassani, 2019. "Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets," Transportation, Springer, vol. 46(6), pages 1975-1996, December.
    3. Tiziana Campisi & Nurten Akgün & Dario Ticali & Giovanni Tesoriere, 2020. "Exploring Public Opinion on Personal Mobility Vehicle Use: A Case Study in Palermo, Italy," Sustainability, MDPI, vol. 12(13), pages 1-15, July.
    4. Alanne, Kari & Cao, Sunliang, 2017. "Zero-energy hydrogen economy (ZEH2E) for buildings and communities including personal mobility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 697-711.
    5. Katarzyna Turoń & Andrzej Kubik & Feng Chen & Hualan Wang & Bogusław Łazarz, 2020. "A Holistic Approach to Electric Shared Mobility Systems Development—Modelling and Optimization Aspects," Energies, MDPI, vol. 13(21), pages 1-19, November.
    6. Bösch, Patrick M. & Becker, Felix & Becker, Henrik & Axhausen, Kay W., 2018. "Cost-based analysis of autonomous mobility services," Transport Policy, Elsevier, vol. 64(C), pages 76-91.
    7. Yeongmin Kwon & Suji Kim & Hyungjoo Kim & Jihye Byun, 2020. "What Attributes Do Passengers Value in Electrified Buses?," Energies, MDPI, vol. 13(10), pages 1-14, May.
    8. Peraphan Jittrapirom & Valeria Caiati & Anna-Maria Feneri & Shima Ebrahimigharehbaghi & María J. Alonso González & Jishnu Narayan, 2017. "Mobility as a Service: A Critical Review of Definitions, Assessments of Schemes, and Key Challenges," Urban Planning, Cogitatio Press, vol. 2(2), pages 13-25.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruomu Miao & Yuxia Wang & Shuang Li, 2021. "Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing," Sustainability, MDPI, vol. 13(2), pages 1-15, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konstanze Winter & Oded Cats & Karel Martens & Bart Arem, 2021. "Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking," Transportation, Springer, vol. 48(4), pages 1931-1965, August.
    2. Becker, Henrik & Balac, Milos & Ciari, Francesco & Axhausen, Kay W., 2020. "Assessing the welfare impacts of Shared Mobility and Mobility as a Service (MaaS)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 228-243.
    3. Pettigrew, Simone & Dana, Liyuwork Mitiku & Norman, Richard, 2019. "Clusters of potential autonomous vehicles users according to propensity to use individual versus shared vehicles," Transport Policy, Elsevier, vol. 76(C), pages 13-20.
    4. Yvonne Hail & Ronald McQuaid, 2021. "The Concept of Fairness in Relation to Women Transport Users," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    5. Sung Il Kwag & Uhjin Hur & Young Dae Ko, 2021. "Sustainable Electric Personal Mobility: The Design of a Wireless Charging Infrastructure for Urban Tourism," Sustainability, MDPI, vol. 13(3), pages 1-15, January.
    6. Marco R. Barassi & Gianluigi De Pascale & Raffaele Lagravinese, 2021. "Testing the law of one-price in the US gasoline market: a long memory approach," SERIES 03-2021, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2021.
    7. Bridgelall, Raj & Stubbing, Edward, 2021. "Forecasting the effects of autonomous vehicles on land use," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    8. Barbora Mazúrová & Ján Kollár & Gabriela Nedelová, 2021. "Travel Mode of Commuting in Context of Subjective Well-Being—Experience from Slovakia," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    9. Liu, Jianing & Wen, Xiao & Jian, Sisi, 2024. "Toward better equity: Analyzing travel patterns through a neural network approach in mobility-as-a-service," Transport Policy, Elsevier, vol. 153(C), pages 110-126.
    10. Ruhrort, Lisa, 2020. "Reassessing the Role of Shared Mobility Services in a Transport Transition: Can They Contribute the Rise of an Alternative Socio-Technical Regime of Mobility?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(19), pages 1-1.
    11. Christian Wankmüller & Maximilian Kunovjanek & Robert Gennaro Sposato & Gerald Reiner, 2020. "Selecting E-Mobility Transport Solutions for Mountain Rescue Operations," Energies, MDPI, vol. 13(24), pages 1-19, December.
    12. Chenhao Zhu & Jonah Susskind & Mario Giampieri & Hazel Backus O’Neil & Alan M. Berger, 2023. "Optimizing Sustainable Suburban Expansion with Autonomous Mobility through a Parametric Design Framework," Land, MDPI, vol. 12(9), pages 1-31, September.
    13. Jiangbo Yu & Graeme McKinley, 2024. "Synthetic Participatory Planning of Shared Automated Electric Mobility Systems," Sustainability, MDPI, vol. 16(13), pages 1-32, June.
    14. Benjamin Maas, 2022. "Literature Review of Mobility as a Service," Sustainability, MDPI, vol. 14(14), pages 1-28, July.
    15. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2021. "Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas," Research in Transportation Economics, Elsevier, vol. 90(C).
    16. Lopez-Carreiro, Iria & Monzon, Andres & Lopez-Lambas, Maria E., 2021. "Comparison of the willingness to adopt MaaS in Madrid (Spain) and Randstad (The Netherlands) metropolitan areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 275-294.
    17. Pangbourne, Kate & Mladenović, Miloš N. & Stead, Dominic & Milakis, Dimitris, 2020. "Questioning mobility as a service: Unanticipated implications for society and governance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 35-49.
    18. Denstadli, Jon Martin & Julsrud, Tom Erik & Christiansen, Petter, 2017. "Urban commuting – A threat to the work-family balance?," Journal of Transport Geography, Elsevier, vol. 61(C), pages 87-94.
    19. Xu Kuang & Fuquan Zhao & Han Hao & Zongwei Liu, 2019. "Assessing the Socioeconomic Impacts of Intelligent Connected Vehicles in China: A Cost–Benefit Analysis," Sustainability, MDPI, vol. 11(12), pages 1-28, June.
    20. Michael Iacono & David Levinson, 2015. "Cohort Effects and Their Influence on Car Ownership," Working Papers 000138, University of Minnesota: Nexus Research Group.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10482-:d:462421. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.