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

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Notes

  1. 1.

    In the same data collection effort, SP surveys were also generated for another mobility survey (Atasoy et al. 2018). The 14 surveys required for each respondent are a mixture of the two (randomly presented with a higher frequency of Tripod appearance).

References

  1. Atasoy, B., Lima de Azevedo, C., Danaf, M., Ding-Mastera, J., Abou-Zeid, M., Cox, N., Zhao, F., Ben-Akiva, M.: Context-aware stated preferences surveys for smart mobility. In: 15th International Conference on Travel Behavior Research (IATBR) (2018)

  2. Azevedo, C.L., Seshadri, R., Gao, S., Atasoy, B., Akkinepally, A.P., Christofa, E., Zhao, F., Trancik, J., Ben-Akiva, M.: Tripod: sustainable travel incentives with prediction, optimization, and personalization. In: Transportation Research Board 97th Annual Meeting (2018)

  3. Ben-Akiva, M., Lerman, S.R.: Discrete choice analysis: theory and application to travel demand. MIT Press, Cambridge (1985)

    Google Scholar 

  4. Ben-Akiva, M., Palma, A.D., Kaysi, I.: Dynamics of commuting decision behavior under advanced traveler information systems. Transp. Res. A 25(5), 251–266 (1991)

    Article  Google Scholar 

  5. Bhuiyan, J.: Uber powered four billion rides in 2017. It wants to do more—and cheaper—in 2018. Recode. https://www.recode.net/2018/1/5/16854714/uber-four-billion-rides-coo-barney-harford-2018-cut-costs-customer-service (2018). Accessed 15 Feb 2019

  6. Bierlaire, M.: BIOGEME: a free package for the estimation of discrete choice models. In: Swiss Transportation Research Conference (2003)

  7. Choudhury, C.F., Yang, L., de Abreu e Silva, J., Ben-Akiva, M.: Modelling preferences for smart modes and services: a case study in Lisbon. Transp. Res. A 115, 15–31 (2017)

    Google Scholar 

  8. Clewlow, R.R.: Carsharing and sustainable travel behavior: results from the San Francisco Bay Area. Transp. Policy 51, 158–164 (2016)

    Article  Google Scholar 

  9. BlaBlaCar: About us. BlaBlaCar. https://blog.blablacar.com/about-us (2019). Accessed 15 Feb 2019

  10. Cottrill, C., Pereira, F., Zhao, F., Dias, I., Lim, H., Ben-Akiva, M., Zegras, P.: Future mobility survey: experience in developing a smartphone-based travel survey in Singapore. Transp. Res. Rec. 2354, 59–67 (2013)

    Article  Google Scholar 

  11. Daly, A., Hess, S., Patruni, B., Potoglou, D., Rohr, C.: Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour. Transportation 39, 267–297 (2012)

    Article  Google Scholar 

  12. Danaf, M., Becker, F., Song, X., Atasoy, B., Ben-Akiva, M.: Online discrete choice models: applications in personalized recommendations. Decis. Support Syst. 119, 35–45 (2019)

    Article  Google Scholar 

  13. Dias, F.F., Lavieri, P.S., Garikapati, V.M., Astroza, S., Pendyala, R.M., Bhat, C.R.: A behavioral choice model of the use of car-sharing and ride-sourcing services. Transportation 44(6), 1307–1323 (2017)

    Article  Google Scholar 

  14. Ghose, A., Han, S.P.: Estimating demand for mobile applications in the new economy. Manag. Sci. 60(6), 1470–1488 (2014)

    Article  Google Scholar 

  15. Halton, J.: On the efficiency of evaluating certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numer. Math. 2, 84–90 (1960)

    Article  Google Scholar 

  16. Jittrapirom, P., Caiati, V., Feneri, A.M., Ebrahimigharehbaghi, S., González, M.J.A., Narayan, J.: Mobility as a service: a critical review of definitions, assessments of schemes, and key challenges. Urban Plan. 2(2), 13–25 (2017)

    Article  Google Scholar 

  17. Kahneman, D., Tversky, A.: Choices, values, and frames. Am. Psychol. 39, 341–350 (1984)

    Article  Google Scholar 

  18. Le Vine, S., Lee-Gosselin, M., Sivakumar, A., Polak, J.: A new approach to predict the market and impacts of round-trip and point-to-point carsharing systems: case study of London. Transp. Res. D 32, 218–229 (2014)

    Article  Google Scholar 

  19. Mahmassani, H.S., Liu, Y.: Dynamics of commuting decision behavior under advanced traveler information systems. Transp. Res. C 7(2–3), 91–107 (1999)

    Article  Google Scholar 

  20. Matyas, M., Kamargianni, M.: The potential of mobility as a service bundles as a mobility management tool. Transportation 45, 1–18 (2018)

    Article  Google Scholar 

  21. McFadden, D.: The measurement of urban travel demand. J. Public Econ. 3(4), 303–328 (1974)

    Article  Google Scholar 

  22. Needell, Z.A., McNerney, J., Chang, M.T., Trancik, J.E.: Potential for widespread electrification of personal vehicle travel in the United States. Nat. Energy 1(9), 16112 (2016)

    Article  Google Scholar 

  23. Pinjari, A.R., Pendyala, R.M., Bhat, C.R., Waddell, P.A.: Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions. Transportation 38, 933–958 (2011)

    Article  Google Scholar 

  24. Plevka, V., Astegiano, P., Himpe, W., Tampère, C., Vandebroek, M.: How personal accessibility and frequency of travel affect ownership decisions on mobility resources. Sustainability 10, 912–936 (2018)

    Article  Google Scholar 

  25. Rasouli, S., Timmermans, H.: Activity-based models of travel demand: promises, progress and prospects. Int. J. Urban Sci. 18(1), 31–60 (2014)

    Article  Google Scholar 

  26. Rayle, L., Dai, D., Chan, N., Cervero, R., Shaheen, S.: Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transp. Policy 45, 168–178 (2016)

    Article  Google Scholar 

  27. Seshadri, R., Kumarga, L., Atasoy, B., Danaf, M., Xie, Y., Lima de Azevedo, C., Zhao, F., Zegras, C., Ben-Akiva, M.: Understanding preferences for automated mobility on demand using a smartphone-based stated preference survey: a case study of Singapore. In: Transportation Research Board 98th Annual Meeting (2019)

  28. Song, X., Danaf, M., Atasoy, B., Ben-Akiva, M.: Personalized menu optimization with preference updater: a Boston case study. Transp. Res. Rec. 2672(8), 599–607 (2018)

    Article  Google Scholar 

  29. United States Census Bureau: American Community Survey (ACS). https://www.census.gov/programs-surveys/acs/. Accessed 5 Nov 5 2018

  30. Viegas de Lima, I., Danaf, M., Akkinepally, A., Lima de Azevedo, C., Ben-Akiva, M.: Modeling framework and implementation of activity-and agent-based simulation: an application to the Greater Boston Area. In: Transportation Research Board 97th Annual Meeting (2018)

    Article  Google Scholar 

  31. Xinhua: DiDi completes 7.43b rides in 2017. China Daily. http://www.chinadaily.com.cn/a/201801/09/WS5a541c98a31008cf16da5e76.html (2018). Accessed 15 Feb 2019

  32. Zhao, F., Ghorpade, A., Pereira, F. C., Zegras, C., Ben-Akiva, M.: Quantifying mobility: pervasive technologies for transport modeling. In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 1039–1044 (2015)

  33. Zoepf, S.M., Keith, D.R.: User decision-making and technology choices in the US carsharing market. Transp. Policy 51, 150–157 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work is funded under the U.S. Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) TRANSNET program, Award Number DE-AR0000611. This work was presented at Transportation Research Board 98th Annual Meeting by the authors. The authors sincerely appreciate the feedbacks received at the conference.

Author information

Affiliations

Authors

Contributions

The authors confirm contribution to the paper as follows: study conception and design: Yifei Xie, Mazen Danaf, Carlos Lima de Azevedo, Arun Prakash Akkinepally, Bilge Atasoy, Ravi Seshadri, Moshe Ben-Akiva; data collection: Yifei Xie, Mazen Danaf, Carlos Lima de Azevedo, Bilge Atasoy, Kyungsoo Jeong; analysis and interpretation of results: Yifei Xie, Mazen Danaf, Carlos Lima de Azevedo, Arun Prakash Akkinepally, Bilge Atasoy, Kyungsoo Jeong, Ravi Seshadri, Moshe Ben-Akiva; draft manuscript preparation: Yifei Xie, Mazen Danaf, Carlos Lima de Azevedo, Arun Prakash Akkinepally, Bilge Atasoy, Kyungsoo Jeong. All authors reviewed the results and approved the final version of the manuscript.

Corresponding author

Correspondence to Yifei Xie.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xie, Y., Danaf, M., Lima Azevedo, C. et al. Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives. Transportation 46, 2017–2039 (2019). https://doi.org/10.1007/s11116-019-10011-z

Download citation

Keywords

  • Smart mobility
  • On-demand
  • Incentives
  • Travel behavior
  • Stated preference
  • Sustainability