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What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

Author

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  • Sujae Kim

    (Department of Urban Planning, Hongik University, Seoul 04066, Korea)

  • Sangho Choo

    (Department of Urban Design & Planning, Hongik University, Seoul 04066, Korea)

  • Sungtaek Choi

    (Department of Metropolitan Transport, The Korea Transport Institute, Sejong 30147, Korea)

  • Hyangsook Lee

    (Graduate School of Logistics, Incheon National University, Incheon 22012, Korea)

Abstract

Mobility as a Service (MaaS), which integrates public and shared transportation into a single service, is drawing attention as a travel demand management strategy aimed at reducing automobile dependency and encouraging public transit. In particular, there have been few studies that recognize traffic congestion during peak hours and identify related factors for practical application. The purpose of this study is to explore what factors affect Seoul commuters’ mode choice including MaaS. A web-based survey that 161 commuters participated in was conducted to collect information about personal, household, and travel attributes, together with their mode preference for MaaS. A latent class model was developed to classify unobserved latent groups based on trip frequency by means and to identify factors influencing mode-specific utilities (in particular, MaaS service) for each class. The result shows that latent classes are divided into two groups (public transit-oriented commuters and balanced mode commuters). Most variables have significant impacts on choice for MaaS. The coefficient of MaaS choice of Class 1 and Class 2 were different. These findings suggest there is a difference between the classes according to trip frequency by means as an influencing factor in MaaS choice.

Suggested Citation

  • Sujae Kim & Sangho Choo & Sungtaek Choi & Hyangsook Lee, 2021. "What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul," Sustainability, MDPI, vol. 13(16), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9324-:d:617657
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    References listed on IDEAS

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    1. Choi, Sungtaek & Mokhtarian, Patricia L., 2020. "How attractive is it to use the internet while commuting? A work-attitude-based segmentation of Northern California commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 37-50.
    2. Ho, Chinh Q. & Hensher, David A. & Mulley, Corinne & Wong, Yale Z., 2018. "Potential uptake and willingness-to-pay for Mobility as a Service (MaaS): A stated choice study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 302-318.
    3. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    4. Melinda Matyas & Maria Kamargianni, 2019. "The potential of mobility as a service bundles as a mobility management tool," Transportation, Springer, vol. 46(5), pages 1951-1968, October.
    5. Joan Walker & Jieping Li, 2007. "Latent lifestyle preferences and household location decisions," Journal of Geographical Systems, Springer, vol. 9(1), pages 77-101, April.
    6. Tang, Wei & Mokhtarian, Patricia L, 2009. "Accounting for Taste Heterogeneity in Purchase Channel Intention Modeling: An Example from Northern California for Book Purchases," Institute of Transportation Studies, Working Paper Series qt9mg5s5g8, Institute of Transportation Studies, UC Davis.
    7. David A. Hensher, 2020. "What might Covid-19 mean for mobility as a service (MaaS)?," Transport Reviews, Taylor & Francis Journals, vol. 40(5), pages 551-556, July.
    8. Mulley, Corinne & Nelson, John D. & Wright, Steve, 2018. "Community transport meets mobility as a service: On the road to a new a flexible future," Research in Transportation Economics, Elsevier, vol. 69(C), pages 583-591.
    9. Kim, Sung Hoo & Mokhtarian, Patricia L., 2018. "Taste heterogeneity as an alternative form of endogeneity bias: Investigating the attitude-moderated effects of built environment and socio-demographics on vehicle ownership using latent class modelin," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 130-150.
    10. Chandra R. Bhat, 1997. "An Endogenous Segmentation Mode Choice Model with an Application to Intercity Travel," Transportation Science, INFORMS, vol. 31(1), pages 34-48, February.
    11. Xiong, Yingge & Mannering, Fred L., 2013. "The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 39-54.
    12. Huo, Jinghai & Yang, Hongtai & Li, Chaojing & Zheng, Rong & Yang, Linchuan & Wen, Yi, 2021. "Influence of the built environment on E-scooter sharing ridership: A tale of five cities," Journal of Transport Geography, Elsevier, vol. 93(C).
    13. 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.
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    Cited by:

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    3. Kriswardhana, Willy & Esztergár-Kiss, Domokos, 2023. "Exploring the aspects of MaaS adoption based on college students’ preferences," Transport Policy, Elsevier, vol. 136(C), pages 113-125.

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