Cited by 184Transportation and Mobility InnovationsElectric Vehicles and InfrastructureEnergy, Environment, and Transportation Policies

Autonomous electric vehicles can reduce carbon emissions and air pollution in cities

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Ercan, Tolga, Onat, Nuri C., Keya, Nowreen, Tatari, Omer, Eluru, Naveen, Kucukvar, Murat·2022·Transportation Research Part D: Transport and Environment

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  • Findings from two studies suggest that the integration of AVs and electric vehicles could significantly contribute to emission reductions and air quality improvements.

    “The findings from two studies suggest that the integration of AVs and electric vehicles could significantly contribute to emission reductions and air quality improvements.”

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  1. Alvanchi, A., Lee, S.H., AbouRizk, S., 2011. Modeling Framework and Architecture of Hybrid System Dynamics and Discrete Event Simulation for Construction. Comput. Civ. Infrastruct. Eng. 26, 77-91. https://doi.org/10.1111/j.1467-8667.2010.00650.x. · DOI
  2. Amaba, B., Dalgetty, B., 2014. Advances in Human Factors, Software, and Systems Engineering. Springer.
  3. Banister, D., 2008. The sustainable mobility paradigm. Transp. Policy 15, 73-80. https://doi.org/10.1016/j.tranpol.2007.10.005. · DOI · Paper page
  4. Beirão, G., Sarsfield Cabral, J.A., 2007. Understanding attitudes towards public transport and private car: A qualitative study. Transp. Policy 14, 478-489. https://doi. org/10.1016/j.tranpol.2007.04.009. · DOI
  5. Brailsford, S.C., Desai, S.M., Viana, J., 2010. Towards the holy grail: Combining system dynamics and discrete-event simulation in healthcare, in Proceedings of the 2010 Winter Simulation Conference. IEEE, pp. 2293-2303.
  6. Brailsford, S., Hilton, N., 2001. A comparison of discrete event simulation and system dynamics for modeling health care systems. Proc. from ORAHS 2000, 18-39. https://doi.org/10.1504/IJBIS.2010.034001. · DOI
  7. Caiazzo, F., Ashok, A., Waitz, I.A., Yim, S.H.L., Barrett, S.R.H., 2013. Air pollution and early deaths in the United States. Part I: Quantifying the impact of major sectors in 2005. Atmos. Environ. 79, 198-208. https://doi.org/10.1016/j.atmosenv.2013.05.081. · DOI
  8. Cheng, Y.-H., Chang, Y.-H., Lu, I.J., 2015. Urban transportation energy and carbon dioxide emission reduction strategies. Appl. Energy 157, 953-973. https://doi.org/ 10.1016/j.apenergy.2015.01.126. · DOI
  9. Ding, C., Wang, D., Liu, C., Zhang, Y., Yang, J., 2017. Exploring the influence of the built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transp. Res. Part A Policy Pract. 100, 65-80. https://doi.org/10.1016/j.tra.2017.04.008. · DOI
  10. Eluru, N., Bhat, C.R., Pendyala, R.M., Konduri, K.C., 2010. A joint flexible econometric model system of household residential location and vehicle fleet composition/ usage choices. Transportation (Amst). 37, 603-626. https://doi.org/10.1007/s11116-010-9271-3. · DOI
  11. Ercan, T., Noori, M., Zhao, Y., Tatari, O., 2016a. On the Front Lines of a Sustainable Transportation Fleet: Applications of Vehicle-to-Grid Technology for Transit and School Buses. Energies 9, 230. https://doi.org/10.3390/en9040230. · DOI
  12. Ercan, T., Onat, N.C., Tatari, O., 2016b. Investigating carbon footprint reduction potential of public transportation in the United States: A system dynamics approach. J. Clean. Prod. 133, 1260-1276. https://doi.org/10.1016/j.jclepro.2016.06.051. · DOI
  13. Ercan, T., Onat, N.C., Tatari, O., Mathias, J.-D., 2017. Public transportation adoption requires a paradigm shift in urban development structure. J. Clean. Prod. 142, 1789-1799. https://doi.org/10.1016/J.JCLEPRO.2016.11.109. · DOI
  14. Ercan, T., Tatari, O., 2015. A hybrid life cycle assessment of public transportation buses with alternative fuel options. Int J Life Cycle Assess 30, 1213-1231. https:// doi.org/10.1007/s11367-015-0927-2. · DOI
  15. Ewing, R., Cervero, R., 2001. Travel and the Built Environment: A Synthesis. Transp. Res. Rec. J. Transp. Res. Board 1780, 87-114. https://doi.org/10.3141/1780-10. · DOI
  16. Fagnant, D.J., Kockelman, K., 2015. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 77, 167-181. https://doi.org/10.1016/j.tra.2015.04.003. · DOI · Paper page
  17. Fishman, G., 2013. Discrete-event simulation: modeling, programming, and analysis. Springer Science & Business Media.
  18. Frank, L.D., Andresen, M.A., Schmid, T.L., 2004. Obesity relationships with community design, physical activity, and time spent in cars. Am. J. Prev. Med. 27, 87-96. https://doi.org/10.1016/j.amepre.2004.04.011. · DOI
  19. Frank, L.D., Sallis, J.F., Conway, T.L., Chapman, J.E., Saelens, B.E., Bachman, W., 2006. Many Pathways from Land Use to Health: Associations between Neighborhood Walkability and Active Transportation, Body Mass Index, and Air Quality. J. Am. Plan. Assoc. 72, 75-87. https://doi.org/10.1080/ 01944360608976725. · DOI
  20. Greenblatt, J.B., Saxena, S., 2015. Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles 5. doi:10.1038/NCLIMATE2685. · DOI · Paper page
  21. Harper, C.D., Hendrickson, C.T., Mangones, S., Samaras, C., 2016. Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions. Transp. Res. Part C Emerg. Technol. 72, 1-9. https://doi.org/10.1016/j.trc.2016.09.003. · DOI
  22. Helal, M., Rabelo, L., Sepulveda, J., Jones, A., 2007. A Methodology for Integrating and Synchronizing the System Dynamics and Discrete Event Simulation Paradigms. Proc. 25th Int. Conf. Syst. Dyn. Soc. 3.
  23. Innocenti, A., Lattarulo, P., Pazienza, M.G., 2013. Car stickiness: Heuristics and biases in travel choice. Transp. Policy 25, 158-168. https://doi.org/10.1016/j. tranpol.2012.11.004. · DOI
  24. Jones, E.C., Leibowicz, B.D., 2019. Contributions of shared autonomous vehicles to climate change mitigation. Transp. Res. Part D Transp. Environ. 72, 279-298. https://doi.org/10.1016/J.TRD.2019.05.005. · DOI · Paper page
  25. Kim, J., Schmöcker, J.D., Fujii, S., Noland, R.B., 2013. Attitudes towards road pricing and environmental taxation among US and UK students. Transp. Res. Part A Policy Pract. 48, 50-62. https://doi.org/10.1016/j.tra.2012.10.005. · DOI
  26. Le Hong, Z., Zimmerman, N., 2021. Air quality and greenhouse gas implications of autonomous vehicles in Vancouver, Canada. Transp. Res. Part D Transp. Environ. 90, 102676 https://doi.org/10.1016/J.TRD.2020.102676. · DOI · Paper page
  27. Litman, T., 2017. Autonomous Vehicle Implementation Predictions: Implications for Transport Planning. https://doi.org/10.1613/jair.301. · DOI · Paper page
  28. Liu, X., Ma, S., Tian, J., Jia, N., Li, G., 2015. A system dynamics approach to scenario analysis for urban passenger transport energy consumption and CO2 emissions: A case study of Beijing. Energy Policy 85, 253-270. https://doi.org/10.1016/j.enpol.2015.06.007. · DOI
  29. McIntosh, J., Trubka, R., Kenworthy, J., Newman, P., 2014. The role of urban form and transit in city car dependence: Analysis of 26 global cities from 1960 to 2000. Transp. Res. Part D Transp. Environ. 33, 95-110. https://doi.org/10.1016/j.trd.2014.08.013. · DOI
  30. Metcalf, G.E., 2009. Designing a carbon tax to reduce U.S. greenhouse gas emissions. Rev. Environ. Econ. Policy 3, 63-83. https://doi.org/10.1093/reep/ren015. · DOI
  31. Milakis, D., Van Arem, B., Van Wee, B., 2017. Policy and society related implications of automated driving: A review of literature and directions for future research. J. Intell. Transp. Syst. Technol. Planning, Oper. 21, 324-348. https://doi.org/10.1080/15472450.2017.1291351. · DOI · Paper page
  32. Morecroft, J., Robinson, S., 2005. Explaining Puzzling Dynamics: Comparing the Use of System Dynamics and Discrete-Event Simulation. Proc. 23rd Int. Conf. Syst. Dyn. Soc, 1-32.
  33. Mueller, K., Sgouris, P.S., 2011. Simulation-based analysis of personal rapid transit systems: service and energy performance assessment of the Masdar City PRT case. J. Adv. Transp. 45, 252-270. https://doi.org/10.1002/atr. · DOI
  34. Newman, P., Kenworthy, J., 2015. The End of Automobile Dependence:, in: The End of Automobile Dependence: How Cities Are Moving Beyond Car-Based Planning. Island Press/Center for Resource Economics, Washington, DC, pp. 201-226. doi:10.5822/978-1-61091-613-4_7. · DOI · Paper page
  35. Noori, M., Tatari, O., 2016. Development of an agent-based model for regional market penetration projections of electric vehicles in the United States. Energy 96, 215-230. https://doi.org/10.1016/j.energy.2015.12.018. · DOI · Paper page
  36. Noori, M., Zhao, Y., Onat, N.C., Gardner, S., Tatari, O., 2016. Light-duty electric vehicles to improve the integrity of the electricity grid through Vehicle-to-Grid technology: Analysis of regional net revenue and emissions savings. Appl. Energy 168, 146-158. https://doi.org/10.1016/j.apenergy.2016.01.030. · DOI
  37. Oakil, A.T.M., Ettema, D., Arentze, T., Timmermans, H., 2014. Changing household car ownership level and life cycle events: An action in anticipation or an action on occurrence. Transportation (Amst). 41, 889-904. https://doi.org/10.1007/s11116-013-9507-0. · DOI
  38. Onat, N.C., Egilmez, G., Tatari, O., 2014. Towards greening the U.S. residential building stock: A system dynamics approach. Build. Environ. 78, 68-80. https://doi. org/10.1016/j.buildenv.2014.03.030. · DOI
  39. Onat, N.C., Kucukvar, M., Halog, A., Cloutier, S., 2017. Systems Thinking for Life Cycle Sustainability Assessment: A Review of Recent Developments, Applications, and Future Perspectives. Sustain. 2017, Vol. 9, Page 706 9, 706. doi:10.3390/SU9050706. · DOI
  40. Onat, N.C., Kucukvar, M., Tatari, O., 2016a. Uncertainty-embedded dynamic life cycle sustainability assessment framework: An ex-ante perspective on the impacts of alternative vehicle options. Energy 112, 715-728. https://doi.org/10.1016/j.energy.2016.06.129. · DOI
  41. Onat, N.C., Kucukvar, M., Tatari, O., Egilmez, G., 2016b. Integration of system dynamics approach toward deepening and broadening the life cycle sustainability assessment framework: a case for electric vehicles. Int. J. Life Cycle Assess. 21, 1009-1034. https://doi.org/10.1007/s11367-016-1070-4. · DOI
  42. Patella, S.M., Scrucca, F., Asdrubali, F., Carrese, S., 2019. Carbon Footprint of autonomous vehicles at the urban mobility system level: A traffic simulation-based approach. Transp. Res. Part D Transp. Environ. 74, 189-200. https://doi.org/10.1016/J.TRD.2019.08.007. · DOI
  43. Peña-Mora, F., Han, S., Lee, S., Park, M., 2008. Strategic-Operational Construction Management: Hybrid System Dynamics and Discrete Event Approach. J. Constr. Eng. Manag. 134, 701-710. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:9(701). · DOI
  44. Potoglou, D., Whittle, C., Tsouros, I., Whitmarsh, L., 2020. Consumer intentions for alternative fuelled and autonomous vehicles: A segmentation analysis across six countries. Transp. Res. Part D Transp. Environ. 79, 102243 https://doi.org/10.1016/J.TRD.2020.102243. · DOI
  45. Poudenx, P., 2008. The effect of transportation policies on energy consumption and greenhouse gas emission from urban passenger transportation. Transp. Res. Part A Policy Pract. 42, 901-909. https://doi.org/10.1016/j.tra.2008.01.013. · DOI
  46. Rees, D., Stephenson, J., Hopkins, D., Doering, A., 2017. Exploring stability and change in transport systems: combining Delphi and system dynamics approaches. Transportation (Amst). 44, 789-805. https://doi.org/10.1007/s11116-016-9677-7. · DOI
  47. Reis, V., 2014. Analysis of mode choice variables in short-distance intermodal freight transport using an agent-based model. Transp. Res. Part A Policy Pract. 61, 100-120. https://doi.org/10.1016/j.tra.2014.01.002. · DOI
  48. Sabounchi, N.S., Triantis, K.P., Sarangi, S., Liu, S., 2014. Dynamic simulation modeling and policy analysis of an area-based congestion pricing scheme for a transportation socioeconomic system. Transp. Res. Part A Policy Pract. 59, 357-383. https://doi.org/10.1016/j.tra.2013.11.007. · DOI
  49. Sarwar, M.T., Anastasopoulos, P.C., Ukkusuri, S.V., Murray-Tuite, P., Mannering, F.L., 2018. A statistical analysis of the dynamics of household hurricane-evacuation decisions. Transportation (Amst). 45, 51-70. https://doi.org/10.1007/s11116-016-9722-6. · DOI
  50. Saunders, M.J., Kuhnimhof, T., Chlond, B., da Silva, A.N.R., 2008. Incorporating transport energy into urban planning. Transp. Res. Part A Policy Pract. 42, 874-882. https://doi.org/10.1016/j.tra.2008.01.031. · DOI
  51. Sener, I.N., Eluru, N., Bhat, C.R., 2009. An analysis of bicycle route choice preferences in Texas. US. Transportation (Amst) 36, 511-539. https://doi.org/10.1007/ s11116-009-9201-4. · DOI
  52. Shafiei, E., Stefansson, H., Asgeirsson, E.I., Davidsdottir, B., Raberto, M., 2013. Integrated Agent-based and System Dynamics Modelling for Simulation of Sustainable Mobility. Transp. Rev. 33, 44-70. https://doi.org/10.1080/01441647.2012.745632. · DOI
  53. Shaheen, S., Cohen, A., 2019. Shared ride services in North America: definitions, impacts, and the future of pooling. Transport Reviews 39 (4), 427-442.
  54. Stern, N.H., 2007. The economics of climate change: the Stern review. Cambridge University Press, Cambridge, UK.
  55. Struben, J., Sterman, J.D., 2008. Transition challenges for alternative fuel vehicle and transportation systems. Environ. Plan. B Plan. Des. 35 (6), 1070-1097.
  56. Wickham, J., Lohan, M., 1999. The Transport Rich and the Transport Poor: Car dependency and social class in four European cities. Pap. Conf. 'Urbanism Suburbanism End Century' Friday, Novemb. 26th Saturday, Novemb. 27th, 1999 1-30. doi:10.1017/CBO9781107415324.004. · DOI · Paper page
  57. WorldBank, 2014. State and Trends of Carbon Pricing, Washington, DC: World Bank. doi:10.1596/978-1-4648-0268-3. · DOI
  58. Zhang, H., Sheppard, C.J.R., Lipman, T.E., Zeng, T., Moura, S.J., 2020. Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas. Transp. Res. Part D Transp. Environ. 78, 102210 https://doi.org/10.1016/J.TRD.2019.102210. · DOI
  59. Zhao, Y., Onat, N.C., Kucukvar, M., Tatari, O., 2016. Carbon and energy footprints of electric delivery trucks: A hybrid multi-regional input-output life cycle assessment. Transp. Res. Part D Transp. Environ. 47, 195-207. https://doi.org/10.1016/j.trd.2016.05.014. · DOI

Cited by

Sorted by citations

Authors
Ercan, T., Onat, N. C., Keya, N., Tatari, O., Eluru, N., & Kucukvar, M.
Affiliations
  • Izmir Institute of Technology (TR)
  • Qatar University (QA)
  • University of Central Florida (US)
Corresponding author
Onat, Nuri C.
Venue
Transportation Research Part D: Transport and Environment
Publisher
Elsevier BV
Published
2022-11
Volume
112
Pages
103472
ISSN
1361-9209 (ISSN-L)
OpenAlex
W4303684342
Cited by
184
References
2
Citations by year
2026: 40 · 2025: 62 · 2024: 47 · 2023: 33 · 2022: 2
FWCI
9.81
Type
journal article
Language
en
Open access
Locations
  • Transportation Research Part D Transport and Environment (publishedVersion)
  • Qatar University QSpace (Qatar University) (submittedVersion)
  • IYTE GCRIS Database (Izmir Institute of Technology) (submittedVersion, other-oa)
Funders
  • US Department of Transportation University Transportation Centers Program
  • U.S. Department of Transportation
  • University of Central Florida (DTRT13-G-UTC51)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
Keywords
autonomous electric vehicles, CO2 emissions, urban air pollution, mode choice, public transport, carpooling, walking and cycling, carbon tax, transport policy, greenhouse gas emissions, shared mobility, electrification, transportation automation, hybrid simulation model

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