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Modeling adoption timing of autonomous vehicles: innovation diffusion approach

Author

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  • Ramin Shabanpour

    (University of Illinois at Chicago)

  • Ali Shamshiripour

    (University of Illinois at Chicago)

  • Abolfazl Mohammadian

    (University of Illinois at Chicago)

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.

Suggested Citation

  • Ramin Shabanpour & Ali Shamshiripour & Abolfazl Mohammadian, 2018. "Modeling adoption timing of autonomous vehicles: innovation diffusion approach," Transportation, Springer, vol. 45(6), pages 1607-1621, November.
    • Handle: RePEc:kap:transp:v:45:y:2018:i:6:d:10.1007_s11116-018-9947-7
    DOI: 10.1007/s11116-018-9947-7
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    References listed on IDEAS

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    1. David C. Schmittlein & Vijay Mahajan, 1982. "Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 1(1), pages 57-78.
    2. Islam, Towhidul & Meade, Nigel, 2012. "The impact of competition, and economic globalization on the multinational diffusion of 3G mobile phones," Technological Forecasting and Social Change, Elsevier, vol. 79(5), pages 843-850.
    3. Roger M. Heeler & Thomas P. Hustad, 1980. "Problems in Predicting New Product Growth for Consumer Durables," Management Science, INFORMS, vol. 26(10), pages 1007-1020, October.
    4. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    7. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    8. Zhang, Junyi & Kuwano, Masashi & Lee, Backjin & Fujiwara, Akimasa, 2009. "Modeling household discrete choice behavior incorporating heterogeneous group decision-making mechanisms," Transportation Research Part B: Methodological, Elsevier, vol. 43(2), pages 230-250, February.
    9. Nourinejad, Mehdi & Bahrami, Sina & Roorda, Matthew J., 2018. "Designing parking facilities for autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 110-127.
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