Skip to main content
SpringerLink
Log in

Modeling adoption timing of autonomous vehicles: innovation diffusion approach

  • Published:
Transportation Aims and scope Submit manuscript

Abstract

Autonomous vehicles (AVs) are expected to act as an economically-disruptive transportation technology offering several benefits to the society and causing significant changes in travel behavior and network performance. However, one of the critical issues that policymakers are facing is the absence of a sound estimation of their market penetration. This study is an effort to quantify the effect of different drivers on the adoption timing of AVs. To this end, we develop an innovation diffusion model in which individuals’ propensities to adopt a new technology such as AVs takes influence from a desire to innovate and a need to imitate the rest of the society. It also captures various sources of inter-personal heterogeneity. We found that conditional on our assumptions regarding the changes in market price of AVs over time, their market penetration in our study region (Chicago metropolitan area) will eventually reach 71.3%. Further, model estimation results show that a wide range of socio-demographic factors, travel pattern indicators, technology awareness, and perceptions of AVs are influential in people’s AV adoption timing decision. For instance, frequent long-distance travelers are found to make the adoption decision more innovatively while those who have experienced an accident in their lifetime are found to be more influenced by word of mouth.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Bain, A.D.: The growth of demand for new commodities. J. R. Stat. Soc. Ser. A. 126, 285–299 (1963)

    Article  Google Scholar 

  • Bass, F.M.: A new product growth for model consumer durables. Manage. Sci. 15, 215–227 (1969). https://doi.org/10.1287/mnsc.15.5.215

    Article  Google Scholar 

  • Dellenback, S.: Director, Intelligent Systems Department, Automation and Data Systems Division, Southwest Research Institute. Commun. by email, May 26. (2013)

  • Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 77, 167–181 (2015)

    Article  Google Scholar 

  • Gregg, J.V., Hossell, C.H., Richardson, J.T.: Mathematical Trend Curves: An Aid to Forecasting. Published for Imperial Chemical Industries by Oliver and Boyd (1966)

  • Heeler, R.M., Hustad, T.P.: Problems in predicting new product growth for consumer durables. Manage. Sci. 26, 1007–1020 (1980). https://doi.org/10.1287/mnsc.26.10.1007

    Article  Google Scholar 

  • Islam, T.: Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data. Energy Policy 65, 340–350 (2014). https://doi.org/10.1016/j.enpol.2013.10.004

    Article  Google Scholar 

  • Islam, T., Meade, N.: The impact of competition, and economic globalization on the multinational diffusion of 3G mobile phones. Technol. Forecast. Soc. Change 79, 843–850 (2012)

    Article  Google Scholar 

  • Kelarestaghi, K.B., Heaslip, K., Fessmann, V., Khalilikhah, M., Fuentes, A.: Intelligent transportation system security: hacked message signs. SAE Int. J. Transp. Cybersecur. Priv. (2018). https://doi.org/10.4271/11-01-02-0004

    Article  Google Scholar 

  • Kyriakidis, M., Happee, R., de Winter, J.C.F.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transp. Res. Part F Traffic Psychol. Behav. 32, 127–140 (2015). https://doi.org/10.1016/j.trf.2015.04.014

    Article  Google Scholar 

  • Langheim, J.: Energy consumption and autonomous driving. Springer, Nueva York, NY (2016). https://doi.org/10.1007/978-3-319-19818-7

    Book  Google Scholar 

  • Lavasani, M., Jin, X., Du, Y.: Market penetration model for autonomous vehicles on the basis of earlier technology adoption experience. Transp. Res. Rec. J. Transp. Res. Board. 2597, 67–74 (2016)

    Article  Google Scholar 

  • Manyika, J., Chui, M., Bughin, J., Dobbs, R., Bisson, P., Marrs, A.: Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy, p. 163. McKinsey Glob. Institute, San Francisco (2013)

    Google Scholar 

  • Mattson, J.W.: Travel Behavior and Mobility of Transportation-Disadvantaged Populations: Evidence from the National Household Travel Survey. Upper Great Plains Transportation Institute, Fargo (2012)

    Google Scholar 

  • McCarthy, C., Ryan, J.: An econometric model of television ownership. Econ. Soc. Rev. 7, 265–277 (1976)

    Google Scholar 

  • Meade, N., Islam, T.: Modelling and forecasting the diffusion of innovation—a 25-year review. Int. J. Forecast. 22, 519–545 (2006). https://doi.org/10.1016/j.ijforecast.2006.01.005

    Article  Google Scholar 

  • Milakis, D., van Arem, B., van Wee, B.: Policy and society related implications of automated driving: a review of literature and directions for future research. J. Intell. Transp. Syst. 21, 324–348 (2017)

    Article  Google Scholar 

  • Mirheli, A., Hajibabai, L., Hajbabaie, A.: Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment. Transp. Res. Part C Emerg. Technol. 92, 412–425 (2018). https://doi.org/10.1016/J.TRC.2018.04.026

    Article  Google Scholar 

  • Nourinejad, M., Bahrami, S., Roorda, M.J.: Designing parking facilities for autonomous vehicles. Transp. Res. Part B Methodol. 109, 110–127 (2018). https://doi.org/10.1016/J.TRB.2017.12.017

    Article  Google Scholar 

  • Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (1962)

    Google Scholar 

  • Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)

    Google Scholar 

  • Ryan, B., Gross, N.C.: The diffusion of hybrid seed corn in two Iowa communities. Rural Sociol. 8, 15–24 (1943)

    Google Scholar 

  • Schmittlein, D.C., Mahajan, V.: Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Mark. Sci. 1, 57–78 (1982)

    Article  Google Scholar 

  • Shabanpour, R., Golshani, N., Auld, J., Mohammadian, A.: Willingness-to-pay for automated vehicles: a random parameters and random thresholds HOPIT model. In: Proceedings of the International Choice Modelling Conference, Cape Town, South Africa (2017)

  • Shabanpour, R., Golshani, N., Shamshiripour, A., Mohammadian, A.K.: Eliciting preferences for adoption of fully automated vehicles using best-worst analysis. Transp. Res. Part C Emerg. Technol. (2018). https://doi.org/10.1016/j.trc.2018.06.014

    Article  Google Scholar 

  • Shamshiripour, A., Golshani, N., Shabanpour, R., Mohammadian, A. (Kouros): Week-long mode choice behavior: a dynamic random effects logit model. In: Transportation Research Board 98th Annual Meeting., Washington, DC (2019)

  • Shchetko, N.: Laser eyes pose price hurdle for driverless cars. Wall Str. J. 21, 2014–2016 (2014)

    Google Scholar 

  • Silberg, G., Wallace, R., Matuszak, G., Plessers, J., Brower, C., Subramanian, D.: Self-driving cars: The next revolution. White paper, KPMG LLP & Center of Automotive Research (2012)

  • Silk, A.J., Urban, G.L.: Pre-test-market evaluation of new packaged goods: a model and measurement methodology. J. Mark. Res. 15, 171–191 (1978)

    Article  Google Scholar 

  • Stephens, T.S., Auld, J., Chen, Y., Gonder, J., Kontou, E., Lin, Z., Xie, F., Mohammadian, A., Shabanpour, R., Gohlke, D.: Assessing energy impacts of connected and automated vehicles at the U.S. national level—preliminary bounds and proposed methods. In: Meyer, G., Beiker, S. (eds.) Road Vehicle Automation 5. Lecture Notes in Mobility, pp. 105–115. Springer, Cham (2019)

    Google Scholar 

  • Tajalli, M., Hajbabaie, A.: Distributed optimization and coordination algorithms for dynamic speed optimization of connected and autonomous vehicles in urban street networks. Transp. Res. Part C Emerg. Technol. 95, 497–515 (2018). https://doi.org/10.1016/J.TRC.2018.07.012

    Article  Google Scholar 

  • Tsai, B.-H., Li, Y., Lee, G.-H.: Forecasting global adoption of crystal display televisions with modified product diffusion model. Comput. Ind. Eng. 58, 553–562 (2010). https://doi.org/10.1016/j.cie.2009.12.002

    Article  Google Scholar 

  • Van den Bulte, C., Stremersch, S.: Social contagion and income heterogeneity in new product diffusion: a meta-analytic test. Mark. Sci. 23, 530–544 (2004). https://doi.org/10.1287/mksc.1040.0054

    Article  Google Scholar 

  • Van Ittersum, K., Feinberg, F.M.: Cumulative timed intent: a new predictive tool for technology adoption. J. Mark. Res. 47, 808–822 (2010)

    Article  Google Scholar 

  • Zhang, J., Kuwano, M., Lee, B., Fujiwara, A.: Modeling household discrete choice behavior incorporating heterogeneous group decision-making mechanisms. Transp. Res. Part B Methodol. 43, 230–250 (2009). https://doi.org/10.1016/j.trb.2008.05.002

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil and Materials Engineering, University of Illinois at Chicago, 842 W. Taylor Street, Chicago, IL, 60607-7023, USA

    Ramin Shabanpour, Ali Shamshiripour & Abolfazl Mohammadian

Authors
  1. Ramin Shabanpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Shamshiripour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abolfazl Mohammadian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Shamshiripour.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shabanpour, R., Shamshiripour, A. & Mohammadian, A. Modeling adoption timing of autonomous vehicles: innovation diffusion approach. Transportation 45, 1607–1621 (2018). https://doi.org/10.1007/s11116-018-9947-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-018-9947-7

Keywords

Search

Navigation