The accelerating emergence of vehicle automation and the anticipation of the advent of shared mobility through fully autonomous vehicles indicate the beginning of a new era of mobility which has the potential to reshape the future of transport in urban areas. In light of such developments, it is important that communities prepare to adapt to the changes they might entail. Therefore, in this paper, traffic flow theory, simulation-based dynamic traffic assignment, and a computer experiment using PTV Visum software were employed to study the impact of different market penetration rates of shared autonomous vehicles (SAVs) on a city-size traffic system. The city of Budapest during morning peak period was chosen as a case study, and a simulation model was created by incorporating SAV elements and their interrelationships into the existing traffic model of the case study city; three alternative future penetration rates were examined in relation to five key performance indicators (KPIs). The simulation results indicated that the implementation of the SAV system has a positive effect on traffic performance. Based on the relationships between the modeled SAV demand shares and the network’s KPIs in the designed scenarios, the overall network performance showed improvement along with an increase in the SAV demand share.
OvertoomI.CorreiaG.HuangY.VerbraeckA.Assessing the Impacts of Shared Autonomous Vehicles on Congestion and Curb Use: A Traffic Simulation Study in The Hague, Netherlands. International Journal of Transportation Science and Technology, Vol. 9, No. 3, 2020, pp. 195–206. https://doi.org/10.1016/j.ijtst.2020.03.009.
2.
BeckerH.CiariF.AxhausenK. W.Comparing Car-Sharing Schemes in Switzerland: User Groups and Usage Patterns. Transportation Research Part A: Policy and Practice, Vol. 97, 2017, pp. 17–29. https://doi.org/10.1016/j.tra.2017.01.004.
3.
FerreroF.PerboliG.RosanoM.VescoA.Car-Sharing Services: An Annotated Review. Sustainable Cities and Society, Vol. 37, 2018, pp. 501–518. https://doi.org/10.1016/j.scs.2017.09.020.
4.
HallJ. V.KruegerA. B.An Analysis of the Labor Market for Uber’s Driver-Partners in the United States. ILR Review, Vol. 71, No. 3, 2017, pp. 705–732. https://doi.org/10.1177/0019793917717222.
ChenT. D.KockelmanK. M.Carsharing’s Life-Cycle Impacts on Energy Use and Greenhouse Gas Emissions. Transportation Research Part D: Transport and Environment, Vol. 47, 2016, pp. 276–284. https://doi.org/10.1016/j.trd.2016.05.012.
8.
MartinE. W.ShaheenS. A.Greenhouse Gas Emission Impacts of Carsharing in North America. IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 4, 2011, pp. 1074–1086. https://doi.org/10.1109/TITS.2011.2158539.
9.
GreenblattJ. B.ShaheenS.Automated Vehicles, On-Demand Mobility, and Environmental Impacts. Current Sustainable/Renewable Energy Reports, Vol. 2, No. 3, 2015, pp. 74–81. https://doi.org/10.1007/s40518-015-0038-5.
10.
WeissJ.HledikR.LuekenR.LeeT.GormanW.The Electrification Accelerator: Understanding the Implications of Autonomous Vehicles for Electric Utilities. The Electricity Journal, Vol. 30, No. 10, 2017, pp. 50–57. https://doi.org/10.1016/j.tej.2017.11.009.
11.
MeszarosF.ShatanawiM.OgunkunbiG. A.Challenges of the Electric Vehicle Markets in Emerging Economies. Periodica Polytechnica Transportation Engineering, Vol. 49, No. 1, 2021, pp. 93–101. https://doi.org/10.3311/PPtr.14037.
12.
FagnantD. J.Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations. Transportation Research Part A: Policy and Practice, Vol. 772015, pp. 167–181.
SimoniM. D.KockelmanK. M.GurumurthyK. M.BischoffJ.Congestion Pricing in a World of Self-Driving Vehicles: An Analysis of Different Strategies in Alternative Future Scenarios. Transportation Research Part C: Emerging Technologies, Vol. 98, 2019, pp. 167–185. https://doi.org/10.1016/j.trc.2018.11.002.
20.
TettamantiT.VargaI.SzalayZ.Impacts of Autonomous Cars from a Traffic Engineering Perspective. Periodica Polytechnica Transportation Engineering, Vol. 44, No. 4, 2016, pp. 244–250. https://doi.org/10.3311/PPtr.9464.
21.
ShatanawiM.GhadiM.MészárosF.Road Pricing Adaptation to Era of Autonomous and Shared Autonomous Vehicles: Perspective of Brazil, Jordan, and Azerbaijan. Transportation Research Procedia, Vol. 55, 2021, pp. 291–298. https://doi.org/10.1016/j.trpro.2021.06.033.
22.
WadudZ.MacKenzieD.LeibyP.Help or Hindrance? The Travel, Energy and Carbon Impacts of Highly Automated Vehicles. Transportation Research Part A: Policy and Practice, Vol. 86, 2016, pp. 1–18. https://doi.org/10.1016/j.tra.2015.12.001.
23.
ShatanawiM.AbdelkhalekF.MészárosF.Urban Congestion Charging Acceptability: An International Comparative Study. Sustainability, Vol. 12, No. 12, 2020, p. 15. https://doi.org/10.3390/su12125044.
24.
NarayananS.ChaniotakisE.AntoniouC.Shared Autonomous Vehicle Services: A Comprehensive Review. Transportation Research Part C: Emerging Technologies, Vol. 111, 2020, pp. 255–293. https://doi.org/10.1016/j.trc.2019.12.008.
MenonN.BarbourN.ZhangY.PinjariA. R.ManneringF.Shared Autonomous Vehicles and Their Potential Impacts on Household Vehicle Ownership: An Exploratory Empirical Assessment. International Journal of Sustainable Transportation, Vol. 13, No. 2, 2019, pp. 111–122. https://doi.org/10.1080/15568318.2018.1443178.
30.
WebbJ.WilsonC.KularatneT.Will People Accept Shared Autonomous Electric Vehicles? A Survey before and after Receipt of the Costs and Benefits. Economic Analysis and Policy, Vol. 61, 2019, pp. 118–135. https://doi.org/10.1016/j.eap.2018.12.004.
31.
International Transport Forum. Safer Roads with Automated Vehicles?International Transport Forum, Leipzig, Germany, 2018.
32.
ZhangW.GuhathakurtaS.FangJ.ZhangG.Exploring the Impact of Shared Autonomous Vehicles on Urban Parking Demand: An Agent-Based Simulation Approach. Sustainable Cities and Society, Vol. 19, 2015, pp. 34–45. https://doi.org/10.1016/j.scs.2015.07.006.
