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Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels

Author

Listed:
  • Vinayak, Pragun
  • Dias, Felipe F.
  • Astroza, Sebastian
  • Bhat, Chandra R.
  • Pendyala, Ram M.
  • Garikapati, Venu M.

Abstract

Activity-travel choices of individuals are influenced by spatial dependency effects. As individuals interact and exchange information with, or observe the behaviors of, those in close proximity of themselves, they are likely to shape their behavioral choices accordingly. For this reason, econometric choice models that account for spatial dependency effects have been developed and applied in a number of fields, including transportation. However, spatial dependence models to date have largely defined the strength of association across behavioral units based on spatial or geographic proximity. In the current context of social media platforms and ubiquitous internet and mobile connectivity, the strength of associations among individuals is no longer solely dependent on spatial proximity. Rather, the strength of associations among individuals may be based on shared attitudes and preferences as well. In other words, behavioral choice models may benefit from defining dependency effects based on attitudinal constructs in addition to geographical constructs. In this paper, frequency of usage of car-sharing and ride-hailing services is modeled using a generalized heterogeneous data model (GHDM) framework that incorporates multi-dimensional dependencies among decision-makers. The model system is estimated on the 2014–2015 Puget Sound Regional Travel Study survey sample, with proximity in latent attitudinal constructs defined by a number of personality trait variables. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.

Suggested Citation

  • Vinayak, Pragun & Dias, Felipe F. & Astroza, Sebastian & Bhat, Chandra R. & Pendyala, Ram M. & Garikapati, Venu M., 2018. "Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels," Transport Policy, Elsevier, vol. 72(C), pages 129-137.
    • Handle: RePEc:eee:trapol:v:72:y:2018:i:c:p:129-137
    DOI: 10.1016/j.tranpol.2018.09.013
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    5. Weibo Li & Maria Kamargianni, 2020. "An Integrated Choice and Latent Variable Model to Explore the Influence of Attitudinal and Perceptual Factors on Shared Mobility Choices and Their Value of Time Estimation," Transportation Science, INFORMS, vol. 54(1), pages 62-83, January.
    6. Gustavo García-Melero & Rubén Sainz-González & Pablo Coto-Millán & Alejandra Valencia-Vásquez, 2021. "Sustainable Mobility Policy Analysis Using Hybrid Choice Models: Is It the Right Choice?," Sustainability, MDPI, vol. 13(5), pages 1-16, March.
    7. Sweet, Matthias N. & Scott, Darren M., 2021. "Shared mobility adoption from 2016 to 2018 in the Greater Toronto and Hamilton Area: Demographic or geographic diffusion?," Journal of Transport Geography, Elsevier, vol. 96(C).
    8. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    9. Jaroslav Mašek & Vladimíra Štefancová & Jaroslav Mazanec & Petra Juránková, 2023. "The Classification of Application Users Supporting and Facilitating Travel Mobility Using Two-Step Cluster Analysis," Mathematics, MDPI, vol. 11(9), pages 1-16, May.

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