Skip to main content
SpringerLink
Log in

Transit user perceptions of driverless buses

  • Published:
Transportation Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price includes VAT (Finland)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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)

    Article  Google Scholar 

  • 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 https://www.rand.org/pubs/research_reports/RR443-2.html (2016)

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Brownstone, D., Train, K.: Forecasting new product penetration with flexible substitution patterns. J. Econom. 89(1–2), 109–129 (1998)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Cervero, R.: Transit pricing research. Transportation 17(2), 117–139 (1990)

    Article  Google Scholar 

  • Daziano, R.A.: Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range. Resour. Energy Econ. 35(3), 429–450 (2013)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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 

  • 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)

    Article  Google Scholar 

  • Guerra, E.: Planning for cars that drive themselves. J. Plan. Educ. Res. 36(2), 210–224 (2016)

    Article  Google Scholar 

  • Horowitz, A.J., Thompson, N.A.: Generic objectives for evaluation of intermodal passenger transfer facilities. Transp. Res. Rec. 1503, 104–110 (1995)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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 https://merritt.cdlib.org/d/ark:%2F13030%2Fm55x29z8/1/producer%2FPRR-2009-28.pdf (2009)

  • Larsen, R.: Feasibility of advanced control systems for transit buses. Transp. Res. Rec. 1604, 155–162 (1997)

    Article  Google Scholar 

  • 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 

  • 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)

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Neff, J., Phamm, L.: A profile of public transportation passenger demographics and travel characteristics reported in on-board surveys. American Public Transportation Association (2007)

  • 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)

    Article  Google Scholar 

  • 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 https://www.its.dot.gov/presentations/CV_PublicMeeting2013/PDF/Day2_Automation.pdf (2013)

  • 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 

  • Schoettle, B., Sivak, M.: Motorists’ preferences for different levels of vehicle automation. The University of Michigan Transportation Research Institute, Ann Arbor (2015)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Train, K.E.: Discrete Choice Methods with Simulation, 2nd edn. Cambridge University Press, New York (2009)

    Book  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Department of City and Regional Planning, University of Pennsylvania, 127 Meyerson Hall, 210 S. 34th Street, Philadelphia, PA, 19104, USA

    Xiaoxia Dong & Erick Guerra

  2. Port Authority of New York and New Jersey, 18th Floor, 150 Greenwich Street, New York, NY, 10007, USA

    Matthew DiScenna

Authors
  1. Xiaoxia Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew DiScenna
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Erick Guerra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoxia Dong.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 109 kb)

Appendix

Appendix

Table with full list of predictor variables included in the final models

Variables

Model 1

Model 2

Model 3

Coefficients (SE)

Coefficients (SE)

Coefficients (SE)

Uncertain

Male

0.193 (0.135)

0.875 (0.228)***

0.386 (0.215)

Income $50,000–$99,999

0.272 (0.179)

0.502 (0.265)

0.304 (0.266)

Income >$100,000

0.193 (0.186)

0.575 (0.282)*

0.170 (0.283)

Age 35–44

0.293 (0.166)

0.203 (0.259)

0.436 (0.264)

Age > 45

−0.403 (0.151)**

−0.923 (0.242)***

−0.718 (0.239)**

Bus usage <2 days per week

0.403 (0.123)**

0.687 (0.196)***

0.655 (0.197)***

Transit usage < everyday

0.179 (0.164)

0.276 (0.242)

0.366 (0.244)

Employee monitoring operations and providing customer services

Not included

1.781 (0.299)***

1.770 (0.298)***

Employee providing customer service but not necessarily monitoring operations

Not included

2.774 (0.345)***

2.805 (0.343)***

Have heard of automated vehicle

Not included

Not included

0.541 (0.237)*

Concern about vehicle safety

Not included

Not included

−2.266 (0.393)***

Concern about lack of assistance for disabled passengers

Not included

Not included

−0.709 (0.206)***

Concern about access to information

Not included

Not included

−0.528 (0.205)**

Intercept

−0.648 (0.170)***

−2.336 (0.327)***

−0.075 (0.435)

Willing

Male

0.756 (0.126)***

2.173 (0.276)***

1.019 (0.260)***

Income $50,000–$99,999

0.186 (0.166)

0.512 (0.330)

0.228 (0.327)

Income >$100,000

0.379 (0.171)*

1.108 (0.351)**

0.325 (0.347)

Age 35–44

−0.293 (0.161)

−0.886 (0.349)*

−0.451 (0.341)

Age > 45

−0.554 (0.140)***

−1.595 (0.299)***

−1.262 (0.291)***

Bus usage < 2 days per week

0.200 (0.117)

0.540 (0.250)*

0.465 (0.247)

Transit usage < Everyday

0.053 (0.161)

0.117 (0.314)

0.425 (0.306)

Employee monitoring operations and providing customer services

Not included

4.860 (0.368)***

4.760 (0.365)***

Employee providing customer service but not necessarily monitoring operations

Not included

4.451 (0.389)***

4.551 (0.388)***

Have heard of automated vehicle

Not included

Not included

1.025 (0.308)***

Concern about vehicle safety

Not included

Not included

−5.914 (0.451)***

Concern about lack of assistance for disabled passengers

Not included

Not included

−0.812 (0.251)**

Concern about access to information

Not included

Not included

−0.943 (0.255)***

Intercept

−0.173 (0.151)

−5.158 (0.454)***

0.246 (0.517)

R 2

0.045

0.258

0.296

Log Likelihood

−2782

−2161

−2049

Significance levels

‘***’0.001

‘**’0.01

‘*’0.05

Predicted responses from model 3 for all variables under different demographic characteristics, transit usage, and employee presence

Variables

Unwilling (%)

Uncertain (%)

Willing (%)

All male

33.1

26.0

40.9

All female

37.3

27.8

34.9

All frequent bus riders

38.6

24.4

36.9

All infrequent bus riders

34.0

29.3

36.7

All frequent transit riders

36.5

26.9

36.6

All infrequent transit riders

33.6

28.6

37.8

All have heard of AV

34.9

27.3

37.8

None has heard of AV

40.1

27.1

32.8

All between age 18 and 34

33.6

25.3

41.1

All between age 35 and 44

31.9

33.6

34.5

All over age 45

40.2

24.6

35.2

Everyone has income below $50 K

37.6

26.2

36.2

Everyone has income between $50 and 100 K

35.4

28.4

36.1

Everyone has income over $100 K

36.0

26.3

37.8

There is an employee onboard in all scenarios

26.1

31.2

42.7

There is no employee onboard in any scenario

54.3

23.7

22.0

There is no employee onboard to monitor and provide customer service in any scenarios

21.6

20.9

57.5

Everyone is concerned about vehicle safety

42.4

32.6

25.0

No one is concerned about vehicle safety

40.4

30.0

29.6

Everyone is concerned about lack of assistance

16.8

15.7

67.6

No one is concerned about lack of assistance

38.7

25.6

35.7

Everyone is concerned about access to info

33.1

28.9

38.0

Not concerned about access to info

38.9

27.0

34.1

There is no employee onboard to monitor and provide customer service in any scenarios

34.0

27.4

38.6

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, X., DiScenna, M. & Guerra, E. Transit user perceptions of driverless buses. Transportation 46, 35–50 (2019). https://doi.org/10.1007/s11116-017-9786-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-017-9786-y

Keywords

Search

Navigation