Transit user perceptions of driverless buses

  • Xiaoxia DongEmail author
  • Matthew DiScenna
  • Erick Guerra


This paper reports the results of a stated preference survey of regular transit users’ willingness to ride and concerns about driverless buses in the Philadelphia region. As automated technologies advance, driverless buses may offer significant efficiency, safety, and operational improvements over traditional bus services. However, unfamiliarity with automated vehicle technology may challenge its acceptance among the general public and slow the adoption of new technologies. Using a mixed logit modeling framework, this research examines which types of transit users are most willing to ride in driverless buses and whether having a transit employee on board to monitor the vehicle operations and/or provide customer service matters. Of the 891 surveyed members of University of Pennsylvania’s transit pass benefit program, two-thirds express a willingness to ride in a driverless bus when a transit employee is on board to monitor vehicle operations and provide customer service. By contrast, only 13% would agree to ride a bus without an employee on board. Males and those in younger age groups (18–34) are more willing to ride in driverless buses than females and those in older age groups. Findings suggest that, so long as a transit employee is onboard, many transit passengers will willingly board early generation automated buses. An abrupt shift to buses without employees on board, by contrast, will likely alienate many transit users.


Automated vehicles Driverless buses Mixed logit Stated preference survey Willingness to ride 



This research was supported by a grant from the U.S. Department of Transportation’s Dwight David Eisenhower Transportation Fellowship Program. Technologies for Safe and Efficient Transportation, a U.S. Department of Transportation University Research Center, also supported this research.

Authors’ contribution

XD: Literature review support, modeling and statistical analysis, writing and editing. MD: Design and management of survey, literature review, writing and editing. EG: Research design support, development of modeling framework, writing and editing.

Supplementary material

11116_2017_9786_MOESM1_ESM.pdf (110 kb)
Supplementary material 1 (PDF 109 kb)


  1. Alessandrini, A., Alfonsi, R., Site, P.D., Stam, D.: Preferences towards automated road public transport: results from European surveys. Transp. Res. Proc. 3, 139–144 (2014)CrossRefGoogle Scholar
  2. Anderson, J.M., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., Oluwatola, T.A.: Autonomous vehicle technology: a guide for policymakers. Santa Monica, CA: RAND Corporation. Retrieved from (2016)
  3. Beggs, S.D., Cardell, N.S.: Choice of smallest car by multi-vehicle households and the demand for electric vehicles. Transp. Res. Part A Gen. 14(5–6), 389–404 (1980)CrossRefGoogle Scholar
  4. Bhat, C.R., Pulugurta, V.: A comparison of two alternative behavioral choice mechanisms for household auto ownership decisions. Transp. Res. Part B Methodol. 32(1), 61–75 (1998)CrossRefGoogle Scholar
  5. Brownstone, D., Train, K.: Forecasting new product penetration with flexible substitution patterns. J. Econom. 89(1–2), 109–129 (1998)CrossRefGoogle Scholar
  6. Calfee, J.E.: Estimating the demand for electric automobiles using fully disaggregated probabilistic choice analysis. Transp. Res. Part B Methodol. 19(4), 287–301 (1985)CrossRefGoogle Scholar
  7. Cervero, R.: Transit pricing research. Transportation 17(2), 117–139 (1990)CrossRefGoogle Scholar
  8. Daziano, R.A.: Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range. Resour. Energy Econ. 35(3), 429–450 (2013)CrossRefGoogle Scholar
  9. Dziekan, K., Kottenhoff, K.: Dynamic at-stop real-time information displays for public transport: effects on customers. Transp. Res. Part A Policy Pract. 41(6), 489–501 (2007)CrossRefGoogle Scholar
  10. Fagnant, D.J., Kockelman, K.M.: The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp. Res. Part C Emerg. Technol. 40, 1–13 (2014)CrossRefGoogle Scholar
  11. Fagnant, D.J., Kockelman, K.M.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 77, 167–181 (2015)CrossRefGoogle Scholar
  12. Fagnant, D.J., Kockelman, K.M.: Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation (2016). doi: 10.1007/s11116-016-9729-z Google Scholar
  13. Fagnant, D.J., Kockelman, K.M., Bansal, P.: Operations of shared autonomous vehicle fleet for Austin, Texas, Market. Transp. Res. Rec. 2536, 98–106 (2015)CrossRefGoogle Scholar
  14. Guerra, E.: Planning for cars that drive themselves. J. Plan. Educ. Res. 36(2), 210–224 (2016)CrossRefGoogle Scholar
  15. Horowitz, A.J., Thompson, N.A.: Generic objectives for evaluation of intermodal passenger transfer facilities. Transp. Res. Rec. 1503, 104–110 (1995)Google Scholar
  16. Jones, L.R., Cherry, C.R., Vu, T.A., Nguyen, Q.N.: The effect of incentives and technology on the adoption of electric motorcycles: a stated choice experiment in Vietnam. Transp. Res. Part A Policy Pract. 57, 1–11 (2013)CrossRefGoogle Scholar
  17. Kalra, N., Anderson, J.M., Wachs, M.: Liability and regulation of autonomous vehicle technologies. California PATH Program, Institute of Transportation Studies, University of California, Berkeley. Retrieved from (2009)
  18. Larsen, R.: Feasibility of advanced control systems for transit buses. Transp. Res. Rec. 1604, 155–162 (1997)CrossRefGoogle Scholar
  19. Lenz, B., Fraedrich, E.: New mobility concepts and autonomous driving: the potential for change. In: Maurer, M., Gerdes, C.J., Lenz, B., Winner, H. (eds.) Autonomous Driving: Technical, Social and Legal Aspects, pp. 173–191. Springer Nature, Berlin (2016)Google Scholar
  20. Lutin, J.M., Kornhauser, A.L.: Application of autonomous driving technology to transit—functional capabilities for safety and capacity. Transportation Research Record, paper number 14-0207 (2014)Google Scholar
  21. Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M., Merat, N.: Acceptance of automated road transport systems (ARTS): an adaptation of the UTAUT model. Transp. Res. Proc. 14, 2217–2226 (2016)CrossRefGoogle Scholar
  22. Mandal, B., Roe, B.E.: Risk tolerance among national longitudinal survey of youth participants: the effects of age and cognitive skills. Economica 81(323), 522–543 (2014)CrossRefGoogle Scholar
  23. Martínez, M., Comejo, J.: Value of the facilities and attributes of new heavy rail and bus rapid transit projects in a developing city: the case of Lima, Peru. Transp. Res. Rec. 1835, 50–58 (2003)CrossRefGoogle Scholar
  24. Neff, J., Phamm, L.: A profile of public transportation passenger demographics and travel characteristics reported in on-board surveys. American Public Transportation Association (2007)Google Scholar
  25. Outwater, M.L., Spitz, G., Lobb, J., Campbell, M., Sana, B., Pendyala, R., Woodford, W.: Characteristics of premium transit services that affect mode choice. Transportation 38(4), 605–623 (2011)CrossRefGoogle Scholar
  26. Schagrin, M., Gay, K.: Developing a U.S. DOT multimodal R&D program plan for road vehicle automation. U.S. Department of Transportation, Presentation. Retrieved from (2013)
  27. Schoettle, B., Sivak, M.: Public opinion about self-driving vehicles in China, India, Japan, the US, the UK, and Australia. The University of Michigan Transportation Research Institute, Ann Arbor (2014)Google Scholar
  28. Schoettle, B., Sivak, M.: Motorists’ preferences for different levels of vehicle automation. The University of Michigan Transportation Research Institute, Ann Arbor (2015)Google Scholar
  29. Taylor, B.D., Miller, D., Iseki, H., Fink, C.: Nature and/or nurture? Analyzing the determinants of transit ridership. Transp Res Part A Policy Pract 43, 60–77 (2009)CrossRefGoogle Scholar
  30. Train, K.E.: Discrete Choice Methods with Simulation, 2nd edn. Cambridge University Press, New York (2009)CrossRefGoogle Scholar
  31. Winston, C., Mannering, F.: Implementing technology to improve public highway performance: a leapfrog technology from the private sector is going to be necessary. Econ Transp 3(2), 158–165 (2014)CrossRefGoogle Scholar
  32. Zhang, W., Guhathakurta, S., Fang, J., Zhang, G.: Exploring the impact of shared autonomous vehicles on urban parking demand: an agent-based simulation approach. Sustain Cities Soc 19, 34–45 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of City and Regional PlanningUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Port Authority of New York and New JerseyNew YorkUSA

Personalised recommendations