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Potential autonomous vehicle ownership growth in Hungary using the Gompertz model

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Autonomous Vehicles (AVs) are anticipated to bring forth a multitude of advantages for upcoming mobility.These potential benefits and many others vary substantially by the market share of AVs. There are several articles that investigated AV market share with a variety of methods, however, they show a huge variation depending on the market specifications. The aim of this research is to calculate private AV adoption rates over time depending on the Hungarian automobile market characteristics. The re-search empirically estimates, using the Gompertz function, the projected growth rates of private auton-omous passenger vehicles in Hungary using historical patterns of human-driven vehicle ownership data on the basis of projected per capita GDP.The study's findings suggest that, in an optimistic and moder-ate scenario, the Hungarian car market is projected to become saturated due to AVs by 2067 and 2076, respectively. However, a pessimistic estimation indicates that saturation is unlikely to occur before 2100. This study’s contribution to the literature is through a mathematical approach that predicts AVs market penetration rate and saturation year, in which the assumptions and the used parameters of the model can be modified depending on different case studies, or they can be updated due to the advancement in technology and improvement in knowledge of the studied market.
Słowa kluczowe
Rocznik
Strony
155--161
Opis fizyczny
Bibliogr. 22 poz., rys.
Twórcy
  • Budapest University of Technology and Economics, 1111 Budapest, Műegyetem rkp. 3, Hungary
autor
  • Budapest University of Technology and Economics, 1111 Budapest, Műegyetem rkp. 3, Hungary; KTI – Institute for Transport Sciences and Logistics, 1119 Budapest, Than Karoly str 3-5, Hungary Tel.: + 36 20 993 2010
Bibliografia
  • 1. Alatawneh, A., Shatanawi, M., Mészáros, F., 2023. Analysis of the Emergence of Autonomous Vehicles Using Simulation-based Dynamic Traffic Assignment – The Case of Budapest. Periodica Polytechnica Transportation Engineering. 51(2), 126–132, DOI: 10.3311/PPtr.20655
  • 2. Dargay, J., Gately, D., Sommer, M., 2007a. Vehicle Ownership and Income Growth, worldwide: 1960-2030. The Energy Journal, 28(4), DOI: 10.5547/ISSN0195-6574-EJ-Vol28-No4-7
  • 3. Eurostat., 2022. https://ec.europa.eu/eurostat
  • 4. Fagnant, D.J., Kockelman, K., 2015. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice. 77, 167–181. DOI: https://doi.org/gc4n5r
  • 5. HCSO., 2021. Regional Statistical Yearbook of Hungary. Hungarian Central Statistical Office. HCSO, https://www.ksh.hu/?lang=enr
  • 6. IHS Automotive., 2014. Emerging Technologies: Autonomous Cars- Not if, but when. https://news.ihsmarkit.com/INFO
  • 7. Kutasi, G., 2022. How Does Economics Approach Nature? Cognitive Sustainability, 1(2), DOI: https://doi.org/jqq3
  • 8. Li, X., Wang, E., Zhang, C., 2014. Prediction of electric vehicle ownership based on Gompertz model. 2014 IEEE International Conference on Information and Automation, (ICIA), 87–91. DOI: https://doi.org/jqq4
  • 9. Litman, T., 2022. Autonomous Vehicle Implementation Predictions. Implications for Transport Planning. Victoria Transport Policy Institute, 48. https://www.vtpi.org/avip.pdf
  • 10. Majerova, J., 2022. Cognitive rationality and sustainable decision based on Maslow’s theorem: A case study in Slovakia. Cognitive Sustainability, 1(1), DOI: https://doi.org/jf2k
  • 11. Matalqah, I., Shatanawi, M., Alatawneh, A., Mészáros, F., 2022. Impact of Different Penetration Rates of Shared Autonomous Vehicles on Traffic: Case Study of Budapest. Transportation Research Record, 03611981221095526. DOI: https://doi.org/jqq5
  • 12. Nadafianshahamabadi, R., Tayarani, M., Rowangould, G., 2021. A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts. Journal of Transport Geography, 94, 103113. DOI: https://doi.org/gkd5pt
  • 13. Rota, M.F., Carcedo, J.M., García, J.P., 2016. Dual approach for modelling demand saturation levels in the automobile market. The Gompertz curve: Macro versus micro data. Investigación Económica, 75(296), 43–72. DOI: https://doi.org/jqq6
  • 14. SAE., 2022. SAE Levels of Driving AutomationTM Refined for Clarity and International Audience. https://www.sae.org/site/blog/sae-j3016-update
  • 15. Shatanawi, M., Alatawneh, A., Mészáros, F., 2022. Implications of static and dynamic road pricing strategies in the era of autonomous and shared autonomous vehicles using simulation-based dynamic traffic assignment: The case of Budapest. Research in Transportation Economics, 95, 101231. DOI: https://doi.org/jqq7
  • 16. Shatanawi, M., Mészáros, F., 2022. Implications of the Emergence of Autonomous Vehicles and Shared Autonomous Vehicles: A Budapest Perspective. Sustainability, 14(17), Article 17. DOI: https://doi.org/jqq8
  • 17. Silva, D., Földes, D., Csiszár, C., 2021. Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method. Sustainability, 13(6), 3008. DOI: https://doi.org/jqq9
  • 18. Talebian, A., Mishra, S., 2018. Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations. Transportation Research Part C: Emerging Technologies, 95, 363–380. DOI: https://doi.org/gfg6tw
  • 19. Talebpour, A., Mahmassani, H. S., 2016. Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71, 143–163. DOI: https://doi.org/f87kqp
  • 20. VOSviewer., 2022. VOSviewer—Visualizing scientific landscapes. https://www.vosviewer.com//
  • 21. Wang, J., Sun, X., He, Y., Hou, S., 2012. Modeling Motorization Development in China. Journal of Transportation Technologies, 02(03), 267–276. DOI: https://doi.org/jqrb
  • 22. World Bank., 2022. World Economic Outlook Databases. https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-344c325c-54e0-4a5d-b8a5-c57a9957d567
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