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Modelling Uptake Sensitivities of Connected and Automated Vehicle Technologies

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  • Gillian Harrison

    (University of Leeds, UK)

  • Simon P. Shepherd

    (University of Leeds, UK)

  • Haibo Chen

    (University of Leeds, UK)

Abstract

Connected and automated vehicle (CAV) technologies and services are rapidly developing and have the potential to revolutionise the transport systems. However, like many innovations, the uptake pathways are uncertain. The focus of this article is on improving understanding of factors that may affect the uptake of highly and fully automated vehicles, with a particular interest in the role of the internet of things (IoT). Using system dynamic modelling, sensitivity testing towards vehicle attributes (e.g., comfort, safety, familiarity) is carried out and scenarios were developed to explore how CAV uptake can vary under different conditions based around the quality of IoT provision. Utility and poor IoT are found to have the biggest influence. Attention is then given to CAV ‘services' that are characterized by the attributes explored earlier in the paper, and it is found that they could contribute to a 20% increase in market share.

Suggested Citation

  • Gillian Harrison & Simon P. Shepherd & Haibo Chen, 2021. "Modelling Uptake Sensitivities of Connected and Automated Vehicle Technologies," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(2), pages 88-106, April.
    • Handle: RePEc:igg:jsda00:v:10:y:2021:i:2:p:88-106
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    References listed on IDEAS

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    1. Marshall, Phil, 2015. "System dynamics modeling of the impact of Internet-of-Things on intelligent urban transportation," 2015 Regional ITS Conference, Los Angeles 2015 146348, International Telecommunications Society (ITS).
    2. Alex Kizito & Agnes Rwashana Semwanga, 2020. "Modeling the Complexity of Road Accidents Prevention: A System Dynamics Approach," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 9(2), pages 24-41, April.
    3. David Pierce & Simon Shepherd & Daniel Johnson, 2019. "Modelling the Impacts of Inter-City Connectivity on City Specialisation," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 8(4), pages 47-70, October.
    4. repec:igg:jsda00:v:8:y:2019:i:4:p:1-24 is not listed on IDEAS
    5. Aalok Kumar & Ramesh Anbanandam, 2019. "Multimodal Freight Transportation Strategic Network Design for Sustainable Supply Chain: An OR Prospective Literature Review," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 8(2), pages 19-35, April.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Paul Pfaffenbichler & Günter Emberger & Simon Shepherd, 2008. "The Integrated Dynamic Land Use and Transport Model MARS," Networks and Spatial Economics, Springer, vol. 8(2), pages 183-200, September.
    8. Gómez Vilchez, Jonatan J. & Jochem, Patrick, 2019. "Simulating vehicle fleet composition: A review of system dynamics models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    9. Jeroen Struben & John D. Sterman, 2008. "Transition Challenges for Alternative Fuel Vehicle and Transportation Systems," Post-Print hal-02312277, HAL.
    10. David E. Boyce & Huw C.W.L. Williams, 2015. "Forecasting Urban Travel," Books, Edward Elgar Publishing, number 13829, December.
    11. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
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    Cited by:

    1. Mao, Wei & Shepherd, Simon & Harrison, Gillian & Xu, Meng, 2024. "Autonomous vehicle market development in Beijing: A system dynamics approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

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