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Prediction of vehicle ownership growth using Gompertz model, case study of Hungary

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Języki publikacji
EN
Abstrakty
EN
Autonomous Vehicles (AVs) are expected to introduce numerous benefits for future mobility. These potential benefits and many others vary substantially by the market share of AVs. Therefore, this research empirically estimates, using the Gompertz function, the projected growth rates of passenger vehicles in Hungary using historical patterns of human-driven vehicle ownership data based on projected per capita GDP. This study’s contribution to the literature is through a mathematical approach that predicts passenger cars market penetration rate, in which the assumptions and the used parameters of the model can be easily modified based on different case studies, or they can be updated due to the advancement in technology and progress in knowledge of the studied market.
Wydawca
Rocznik
Strony
164--169
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
  • Budapest University of Technology and Economics, Hungary
Bibliografia
  • 1. Dargay, J., Gately, D., Sommer, M., 2007. Vehicle ownership and income growth, worldwide: 1960-2030. The energy journal, 28(4). DOI: 10.5547/ISSN0195-6574- EJ-Vol28-No4-7.
  • 2. Eurostat, 2022. URL: https://ec.europa.eu/eurostat (accessed Apr. 12, 2022).
  • 3. 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: 10.1016/j.tra.2015.04.003.
  • 4. Felis Rota, M., Moral Carcedo, J., Pérez García, J, 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: 10.1016/j.inveco.2016.07.003.
  • 5. Hungarian Central Statistical Office, 2022. URL: https://www.ksh.hu/?lang=en (accessed Apr. 12, 2022).
  • 6. Li, X., Wang, E., Zhang, C., 2014. Prediction of electric vehicle ownership based on Gompertz model. In 2014 IEEE International Conference on Information and Automation (ICIA) (pp. 87-91). IEEE. DOI: 10.1109/ICInfA.2014.6932631.
  • 7. 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: 10.1016/j.jtrangeo.2021.103113.
  • 8. SAE Levels of Driving AutomationTM Refined for Clarity and International Audience’, 2022. https://www.sae.org/site/blog/sae-j3016-update (accessed Apr. 12, 2022).
  • 9. 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: 10.1016/j.trc.2018.06.005.
  • 10. 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: 10.1016/j.trc.2016.07.007.
  • 11. Wang, J., Sun, X., He, Y., Hou, S., 2012. Modeling motorization development in China. Journal of Transportation Technologies, 2(03), 267. DOI: 10.4236/jtts.2012.23029.
  • 12. World Economic Outlook, 2022. Databases - IMF, URL: https://www.imf.org/en/Publications/SPROLLs/world-economic-outlookdatabases (accessed Apr. 12, 2022)
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-2f4dcad1-3f47-4f39-b368-a429fea78478
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