Examining the Impact of Hysteresis on the Projected Adoption of Autonomous Vehicles

Anas Alatawneh; Adam Torok

doi:10.7307/ptt.v35i5.278

Authors

Anas Alatawneh Budapest University of Technology and Economics
Adam Torok Budapest University of Technology and Economics; KTI – Institute for Transport Sciences and Logistics

DOI:

https://doi.org/10.7307/ptt.v35i5.278

Keywords:

autonomous vehicle, GDP, hysteresis, traffic flow forecasting, adoption, diffusion

Abstract

This study explores the potential impact of per capita gross domestic product (GDP) changes on the adoption of autonomous vehicles (AVs). The level of adoption of AVs is anticipated to influence the benefits of future mobility, prompting numerous studies that forecast the market share of AVs using various methods. The influence of changes in the per capita GDP on vehicle ownership is crucial in assessing the challenges associated with reducing dependence on AVs in the future. This phenomenon, known as the hysteresis effect, implies that AV adoption estimates may differ when the GDP is rising as opposed to when it is falling. This research examines the effect of rising and falling GDP per capita on the anticipated AV diffusion in Hungary, utilising a scenario-based method to account for the variation in adoption rates in the literature. The study findings indicate that declines in GDP in the past will impact AV ownership, leading to a shift in future adoption patterns. The AV market is projected to reach saturation in the 2070s and the 2090s in favourable and moderate scenarios, respectively, while a pessimistic state would delay this outcome until after the year 2100.

References

Fagnant DJ, Kockelman K. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice. 2015;77:167-181. DOI: 10.1016/j.tra.2015.04.003.

Talebpour A, Mahmassani HS. Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies. 2016;71:143-163. DOI: 10.1016/j.trc.2016.07.007.

SAE. SAE levels of driving automationTM refined for clarity and international audience. https://www.sae.org/site/blog/sae-j3016-update [Accessed 12th Apr. 2022].

Alatawneh A, Shatanawi M, Mészáros F. Analysis of the emergence of autonomous vehicles using simulation-based dynamic traffic assignment – The case of Budapest. Periodica Polytechnica Transportation Engineering. 2023;51(2):126-132. DOI: 10.3311/PPtr.20655.

Nadafianshahamabadi R, Tayarani M, Rowangould G. A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts. Journal of Transport Geography. 2021;94(C):103-113. DOI: 10.1016/j.jtrangeo.2021.103113.

Shatanawi M, Ghadi M, Mészáros F. Road pricing adaptation to era of autonomous and shared autonomous vehicles: Perspective of Brazil, Jordan, and Azerbaijan. Transportation Research Procedia. 2021;55:291-298. DOI: 10.1016/j.trpro.2021.06.033.

Torok A, Pauer G. Safety aspects of critical scenario identification for autonomous transport. Cognitive Sustainability. 2022;1(3). DOI: 10.55343/cogsust.23.

Krizsik N, Sipos T. Social perception of autonomous vehicles. Periodica Polytechnica Transportation Engineering. 2023;51(2):133-139. DOI: 10.3311/PPtr.20228.

Litman T. Autonomous vehicle implementation predictions. implications for transport planning. https://www.vtpi.org/avip.pdf [Accessed 6th Mar. 2022].

Matalqah I, Shatanawi M, Alatawneh A, Mészáros F. Impact of different penetration rates of shared autonomous vehicles on traffic: Case study of Budapest. Transportation Research Record. 2022;2676(12):396-408. DOI: 10.1177/03611981221095526.

Shatanawi M, Alatawneh A, Mészáros F. 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. 2022;95:101231. DOI: 10.1016/j.retrec.2022.101231.

Shatanawi M, Mészáros F. Implications of the emergence of autonomous vehicles and shared autonomous vehicles: A Budapest perspective. Sustainability. 2022;14(17):10952. DOI: 10.3390/su141710952.

Hamadneh J, Esztergar-Kiss D. Evaluation of the impacts of autonomous vehicles on the mobility of user groups by using agent-based simulation. Transport. 2022;37(1):1-16. DOI: 10.3846/transport.2022.16322.

Gocke M. Various concepts of hysteresis applied in economics. J Economic Surveys. 2002;16(2):167-188. DOI: 10.1111/1467-6419.00163.

Kutasi G, Érfalvy Á, Torda S, Golenyák V. The economic effects of climate change on Budapest. Cognitive Sustainability. 2023;2(1). DOI: 10.55343/cogsust.34.

Dargay JM. The effect of income on car ownership: Evidence of asymmetry. Transportation Research Part A. 2001;35(9):807-821. DOI: 10.1016/S0965-8564(00)00018-5.

Eurostat - The statistical office of the European Union. Passenger cars per 1000 inhabitants. https://ec.europa.eu/eurostat [Accessed 12th Apr. 2022].

HCSO - Hungarian Central Statistical Office. Regional statistical yearbook of Hungary. https://www.ksh.hu/?lang=en [Accessed 12th Apr. 2022].

International Monetary Fund. World economic outlook databases. https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases [Accessed 12th Apr. 2022].

Pendyala RM, Kostyniuk LP, Goulias KG. A repeated cross-sectional evaluation of car ownership. Transportation. 1995;22(2):165-184. DOI: 10.1007/BF01099438.

Trommer S, Kröger L, Kuhnimhof T. Potential fleet size of private autonomous vehicles in Germany and the US. In: Meyer G, Beiker S, eds. Road Vehicle Automation 4. Lecture Notes in Mobility. Springer International Publishing; 2018. p. 247-256. DOI: 10.1007/978-3-319-60934-8_20.

Talebian A, Mishra S. Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations. Transportation Research Part C: Emerging Technologies. 2018;95:363-380. DOI: 10.1016/j.trc.2018.06.005.

Chen Z, He F, Zhang L, Yin Y. Optimal deployment of autonomous vehicle lanes with endogenous market penetration. Transportation Research Part C: Emerging Technologies. 2016;72:143-156. DOI: 10.1016/j.trc.2016.09.013.

Noruzoliaee M, Zou B, Liu Y. Roads in transition: Integrated modeling of a manufacturer-traveler-infrastructure system in a mixed autonomous/human driving environment. Transportation Research Part C: Emerging Technologies. 2018;90:307-333. DOI: 10.1016/j.trc.2018.03.014.

Bansal P, Kockelman KM. Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. Transportation Research Part A: Policy and Practice. 2017;95:49-63. DOI: 10.1016/j.tra.2016.10.013.

El Zarwi F, Vij A, Walker JL. A discrete choice framework for modeling and forecasting the adoption and diffusion of new transportation services. Transportation Research Part C: Emerging Technologies. 2017;79:207-223. DOI: 10.1016/j.trc.2017.03.004.

Shatanawi M, Hajouj M, Edries B, Mészáros F. The interrelationship between road pricing acceptability and self-driving vehicle adoption: Insights from four countries. Sustainability. 2022;14(19):12798. DOI: 10.3390/su141912798.

Lavasani M, Jin X, Du Y. Market penetration model for autonomous vehicles on the basis of earlier technology adoption experience. Transportation Research Record. 2016;2597(1):67-74. DOI: 10.3141/2597-09.

VOSviewer - Visualizing scientific landscapes. https://www.vosviewer.com// [Accessed 26th Oct. 2022].

Eck LW, Waltman L. VOSviewer Manual. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.18.pdf [Accessed 26th Oct. 2022].

Cross R, ed. Unemployment, hysteresis, and the natural rate hypothesis. Blackwell; 1988.

Li X, Wang E, Zhang C. Prediction of electric vehicle ownership based on Gompertz model. 2014 IEEE International Conference on Information and Automation (ICIA). IEEE; 2014. p. 87-91. DOI: 10.1109/ICInfA.2014.6932631.

Alatawneh A, Torok A. Potential autonomous vehicle ownership growth in Hungary using the Gompertz model. Production Engineering Archives. 2023;29(2):155-161. DOI: 10.30657/pea.2023.29.18.

Dargay J, Gately D, Sommer M. Vehicle ownership and income growth, worldwide: 1960-2030. EJ. 2007;28(4). DOI: 10.5547/ISSN0195-6574-EJ-Vol28-No4-7.

Wang J, Sun X, He Y, Hou S. Modeling motorization development in China. JTTs. 2012;02(03):267-276. DOI: 10.4236/jtts.2012.23029.

Rota MF, Carcedo JM, García JP. Dual approach for modelling demand saturation levels in the automobile market. The Gompertz curve: Macro versus micro data. Investigación Económica. 2016;75(296):43-72. DOI: 10.1016/j.inveco.2016.07.003.

Silva D, Földes D, Csiszár C. Autonomous vehicle use and urban space transformation: A scenario building and analysing method. Sustainability. 2021;13(6):3008. DOI: 10.3390/su13063008.

Voulgaris CT. Crystal balls and black boxes: What makes a good forecast? Journal of Planning Literature. 2019;34(3):286-299. DOI: 10.1177/0885412219838495.

Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 2021;7:e623. DOI: 10.7717/peerj-cs.623.