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Journal of Intelligent Transportation Systems
Technology, Planning, and Operations
Volume 27, 2023 - Issue 4
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Articles

Modeling the long-term regional impacts of autonomous vehicles: A case study of Victoria, Australia

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Pages 459-470 | Received 28 Feb 2021, Accepted 16 Dec 2021, Published online: 23 Mar 2022

References

  • AAA (2015). Driving costs. http://exchange.aaa.com/wp26content/uploads/2015/04/Your-Driving-Costs-2015.pdf
  • Alazzawi, S., Hummel, M., Kordt, P., Sickenberger, T., Wieseotte, C., & Wohak, O. (2018). Simulating the impact of shared, autonomous vehicles on urban mobility-a case study of Milan [Paper presentation]. SUMO User Conference.
  • Anderson, J. M., Nidhi, K., Stanley, K. D., Sorensen, P., Samaras, C., & Oluwatola, O. A. (2014). Autonomous vehicle technology: A guide for policymakers. Rand Corporation.
  • Bansal, P., & Kockelman, K. M. (2017). Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. Transportation Research Part A: Policy and Practice, 95, 49–63. https://doi.org/10.1016/j.tra.2016.10.013
  • Bauer, G. S., Greenblatt, J. B., & Gerke, B. F. (2018). Cost, energy, and environmental impact of automated electric taxi fleets in Manhattan. Environmental Science & Technology, 52(8), 4920–4928. https://doi.org/10.1021/acs.est.7b04732
  • Bischoff, J., Maciejewski, M., & Nagel, K. (2017). City-wide shared taxis: A simulation study in Berlin [Paper presentation]. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE. https://doi.org/10.1109/ITSC.2017.8317926
  • Chen, T. D., & Kockelman, K. M. (2016). Management of a shared autonomous electric vehicle fleet: Implications of pricing schemes. Transportation Research Record: Journal of the Transportation Research Board, 2572(1), 37–46. https://doi.org/10.3141/2572-05
  • Childress, S., Nichols, B., Charlton, B., & Coe, S. (2015). Using an activity-based model to explore the potential impacts of automated vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2493(1), 99–106. https://doi.org/10.3141/2493-11
  • Craig Mcpherson, P. B. (2010). Recalibration and revalidation of the Melbourne integrated transport model.
  • Daly, A. (1987). Estimating “tree” logit models. Transportation Research Part B: Methodological, 21(4), 251–267. https://doi.org/10.1016/0191-2615(87)90026-9
  • Department of Economic Development. (2017). Jobs, Transport and Resources. Victorian Integrated Survey of Travel and Activity (VISTA). [Online] Available at: http://economicdevelopement.vic.gov.au/transport/research-and-data/vista
  • 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. https://doi.org/10.1016/j.tra.2015.04.003
  • Fagnant, D. J., & Kockelman, K. M. (2018). Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation, 45(1), 143–158. https://doi.org/10.1007/s11116-016-9729-z
  • Fernandes, P., Bandeira, J. M., & Coelho, M. C. (2021). A macroscopic approach for assessing the environmental performance of shared, automated, electric mobility in an intercity corridor. Journal of Intelligent Transportation Systems, 7, 1–17. https://doi.org/10.1080/15472450.2021.1945453
  • Fraedrich, E., Heinrichs, D., Bahamonde-Birke, F. J., & Cyganski, R. (2019). Autonomous driving, the built environment and policy implications. Transportation Research Part A: policy and Practice, 122, 162–172. https://doi.org/10.1016/j.tra.2018.02.018
  • Frank, M., & Wolfe, P. (1956). An algorithm for quadratic programming. Naval Research Logistics Quarterly, 3(1–2), 95–110. https://doi.org/10.1002/nav.3800030109
  • Friedrich, M., Sonnleitner, J., & Richter, E. (2019). Integrating automated vehicles into macroscopic travel demand models. Transportation Research Procedia, 41, 360–375. https://doi.org/10.1016/j.trpro.2019.09.060
  • Hörl, S. (2017). Agent-based simulation of autonomous taxi services with dynamic demand responses. Procedia Computer Science, 109, 899–904. https://doi.org/10.1016/j.procs.2017.05.418
  • Kim, K.-H., Yook, D.-H., Ko, Y.-S., & Kim, D. (2015). An analysis of expected effects of the autonomous vehicles on transport and land use in Korea. Marron Institute of Urban Management.
  • Kumar, A., & Peeta, S. (2013). A post-processing technique for the four-step travel demand modeling executed through a feedback loop. Procedia – Social and Behavioral Sciences, 104(0), 611–620. https://doi.org/10.1016/j.sbspro.2013.11.155
  • Lavasani, M., Jin, X., & Du, Y. (2016). Market penetration model for autonomous vehicles on the basis of earlier technology adoption experience. Transportation Research Record: Journal of the Transportation Research Board, 2597(1), 67–74. https://doi.org/10.3141/2597-09
  • Levin, M. W., & Boyles, S. D. (2015). Effects of autonomous vehicle ownership on trip, mode, and route choice. Transportation Research Record: Journal of the Transportation Research Board, 2493(1), 29–38. https://doi.org/10.3141/2493-04
  • Litman, T. (2014). Autonomous vehicle implementation predictions. Victoria Transport Policy Institute, 28(2014).
  • Litman, T. (2017). Autonomous vehicle implementation predictions (p. 28). Victoria Transport Policy Institute.
  • Liu, J., Kockelman, K. M., Boesch, P. M., & Ciari, F. (2017). Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation. Transportation, 44(6), 1261–1278. https://doi.org/10.1007/s11116-017-9811-1
  • Loeb, B., Kockelman, K. M., & Liu, J. (2018). Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions. Transportation Research Part C: Emerging Technologies, 89, 222–233. https://doi.org/10.1016/j.trc.2018.01.019
  • Lokhandwala, M., & Cai, H. (2018). Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC. Transportation Research Part C: Emerging Technologies, 97, 45–60. https://doi.org/10.1016/j.trc.2018.10.007
  • Martinez, L. M., & Viegas, J. M. (2017). Assessing the impacts of deploying a shared self-driving urban mobility system: An agent-based model applied to the city of Lisbon, Portugal. International Journal of Transportation Science and Technology, 6(1), 13–27. https://doi.org/10.1016/j.ijtst.2017.05.005
  • Moreno, A. T., Michalski, A., Llorca, C., & Moeckel, R. (2018). Shared autonomous vehicles effect on vehicle-km traveled and average trip duration. Journal of Advanced Transportation, 2018, 1–10. https://doi.org/10.1155/2018/8969353
  • Narayanan, S., Chaniotakis, E., & Antoniou, C. (2020). Shared autonomous vehicle services: A comprehensive review. Transportation Research Part C: Emerging Technologies, 111, 255–293. https://doi.org/10.1016/j.trc.2019.12.008
  • National Highway Traffic Safety Administration (2011). USDOT connected vehicle research 32 program: Vehicle-to-vehicle safety application research plan. Dot Hs, 811, 373.
  • NHTSA, U.S. Department of Transportation Releases Policy on Autonomous Vehicle Development. 2013. http://www.nhtsa.gov/About+NHTSA/Press+Releases/U.S.+Department+of+Transportation+Releases+Policy+on+Automated+Vehicle+Development.
  • Olia, A., Razavi, S., Abdulhai, B., & Abdelgawad, H. (2018). Traffic capacity implications of automated vehicles mixed with regular vehicles. Journal of Intelligent Transportation Systems, 22(3), 244–262. https://doi.org/10.1080/15472450.2017.1404680
  • Shaheen, S., & Chan, N. (2016). Mobility and the sharing economy: Potential to facilitate the first-and last-mile public transit connections. Built Environment, 42(4), 573–588. https://doi.org/10.2148/benv.42.4.573
  • Shladover, S. E. (2009). Cooperative (rather than autonomous) vehicle-highway 557 automation systems (pp. 10–19).
  • Simoni, M. D., Kockelman, K. M., Gurumurthy, K. M., & Bischoff, J. (2019). Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios. Transportation Research Part C: Emerging Technologies, 98, 167–185. https://doi.org/10.1016/j.trc.2018.11.002
  • Spiridonos, F. (2013). Transport demand modelling in Melbourne. WIT Transactions on the Built Environment, 130, 331–347.
  • Tientrakool, P., Ho, Y., & Maxemchuk, N. F. (2011). Highway Capacity Benefits from Using 564 Vehicle-to-Vehicle Communication and Sensors for Collision Avoidance [Paper presentation]. Vehicular Technology 565 Conference, IEEE., 2011 https://doi.org/10.1109/VETECF.2011.6093130
  • Truong, L. T., De Gruyter, C., Currie, G., & Delbosc, A. (2017). Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria, Australia. Transportation, 44(6), 1279–1292. https://doi.org/10.1007/s11116-017-9802-2
  • Zhang, W., Guhathakurta, S., Fang, J., & Zhang, G. (2015). Exploring the impact of shared autonomous vehicles on urban parking demand: An agent-based simulation approach. Sustainable Cities and Society, 19, 34–45. https://doi.org/10.1016/j.scs.2015.07.006
  • Zhao, Y., & Kockelman, K. M. (2018). Anticipating the regional impacts of connected and automated vehicle travel in Austin, Texas. Journal of Urban Planning and Development, 144(4), 04018032. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000463

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