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Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

Author

Listed:
  • Yifei Xie

    (Massachusetts Institute of Technology)

  • Mazen Danaf

    (Massachusetts Institute of Technology)

  • Carlos Lima Azevedo

    (Technical University of Denmark)

  • Arun Prakash Akkinepally

    (Massachusetts Institute of Technology)

  • Bilge Atasoy

    (Delft University of Technology)

  • Kyungsoo Jeong

    (Transportation and Hydrogen Systems Center, National Renewable Energy Laboratory)

  • Ravi Seshadri

    (Singapore-MIT Alliance for Research and Technology (SMART))

  • Moshe Ben-Akiva

    (Massachusetts Institute of Technology)

Abstract

This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet inter-connected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture models with inter-consumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modeling framework is essential for accounting the impacts of real-time on-demand system’s dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multi-modal dynamic simulators. The methodology is applied to a case study of an innovative personalized on-demand real-time system which incentivizes travelers to select more sustainable travel options. The data for model estimation is collected through a smartphone-based context-aware stated preference survey. Through model estimation, lower values of time are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. These results are fundamental in setting the ground for different behavioral scenarios of such a new on-demand system. The proposed methodology is flexible to be applied to model other on-demand mobility services such as ride-hailing services and the emerging mobility as a service.

Suggested Citation

  • Yifei Xie & Mazen Danaf & Carlos Lima Azevedo & Arun Prakash Akkinepally & Bilge Atasoy & Kyungsoo Jeong & Ravi Seshadri & Moshe Ben-Akiva, 2019. "Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives," Transportation, Springer, vol. 46(6), pages 2017-2039, December.
    • Handle: RePEc:kap:transp:v:46:y:2019:i:6:d:10.1007_s11116-019-10011-z
    DOI: 10.1007/s11116-019-10011-z
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    2. Antonella Franco & Antonino Vitetta, 2023. "Preference Model in the Context of Mobility as a Service: A Pilot Case Study," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    3. Umer Zaman & Syed Hassan Raza & Saba Abbasi & Murat Aktan & Pablo Farías, 2021. "Sustainable or a Butterfly Effect in Global Tourism? Nexus of Pandemic Fatigue, COVID-19-Branded Destination Safety, Travel Stimulus Incentives, and Post-Pandemic Revenge Travel," Sustainability, MDPI, vol. 13(22), pages 1-21, November.
    4. Schasché, Stephanie E. & Sposato, Robert G. & Hampl, Nina, 2022. "The dilemma of demand-responsive transport services in rural areas: Conflicting expectations and weak user acceptance," Transport Policy, Elsevier, vol. 126(C), pages 43-54.
    5. Dubey, Subodh & Cats, Oded & Hoogendoorn, Serge & Bansal, Prateek, 2022. "A multinomial probit model with Choquet integral and attribute cut-offs," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 140-163.
    6. Rico Krueger & Taha H. Rashidi & Akshay Vij, 2019. "Semi-Parametric Hierarchical Bayes Estimates of New Yorkers' Willingness to Pay for Features of Shared Automated Vehicle Services," Papers 1907.09639, arXiv.org.
    7. Suprava Chakraborty & Nallapaneni Manoj Kumar & Arunkumar Jayakumar & Santanu Kumar Dash & Devaraj Elangovan, 2021. "Selected Aspects of Sustainable Mobility Reveals Implementable Approaches and Conceivable Actions," Sustainability, MDPI, vol. 13(22), pages 1-31, November.
    8. Cantelmo, Guido & Amini, Roja Ezzati & Monteiro, Mayara Moraes & Frenkel, Amnon & Lerner, Ofer & Tavory, Sharon Shoshany & Galtzur, Ayelet & Kamargianni, Maria & Shiftan, Yoram & Behrischi, Christiane, 2022. "Aligning users’ and stakeholders’ needs: How incentives can reshape the carsharing market," Transport Policy, Elsevier, vol. 126(C), pages 306-326.
    9. Youssef M Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2020. "Sparse Covariance Estimation in Logit Mixture Models," Papers 2001.05034, arXiv.org.
    10. Alexandros Nikitas & Kalliopi Michalakopoulou & Eric Tchouamou Njoya & Dimitris Karampatzakis, 2020. "Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    11. Abolfazl Dehghanmongabadi & Åžebnem HoÅŸkara, 2020. "Determinative Variables Toward Promoting Use of Active Modes of Transportation: Enhancing Level of Sustainable Mobility in Communities," SAGE Open, , vol. 10(3), pages 21582440209, September.
    12. Paulo Antonio Maldonado Silveira Alonso Munhoz & Fabricio da Costa Dias & Christine Kowal Chinelli & André Luis Azevedo Guedes & João Alberto Neves dos Santos & Wainer da Silveira e Silva & Carlos Alb, 2020. "Smart Mobility: The Main Drivers for Increasing the Intelligence of Urban Mobility," Sustainability, MDPI, vol. 12(24), pages 1-25, December.

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