How Automated Vehicles affects Transportation Systems Operations

Many researchers have used agent-based simulation to assess the effects of Automated Vehicles (AV)s on transportation system operations and efficiency (e.g., congestion and Vehicle Miles Traveled (VMT)) [1], [2], [3], [4], [5], [6], [7]. For example, Yan et al. (2020) simulated and then evaluated the performance of a shared autonomous vehicle fleet serving requests across the Minneapolis-Saint Paul region [1]. Yan et al. [1], [2], [3], [4], [5], [6], [7] estimated that the average shared AV could serve at most 30 person-trips per day with less than a 5 minute wait time but generates 13 percent more VMT. Yan et al. [1], [2], [3], [4], [5], [6], [7] also concluded that dynamic ridesharing could reduce shared AV VMT by 17 percent on average and restricting shared AV parking on the busiest streets could generate up to 8 percent more VMT.
Other methods such as static traffic assignment models and scenario analysis, have also been used to to understand the effect of AVs on congestion and VMT [8], [9], [10], [11], [12], [13]. For example, Harper et al. (2016) estimated the upper bound increase in travel with AVs for the non-driving, elderly, and people with travel-restrictive medical conditions by creating demand wedges and assuming that these traditionally underserved populations would travel as much as younger and/or healthier populations [9]. Harper et al. (2016) estimated that vehicle automation addressing latent demand for underserved population could increase VMT by as much as 14 percent, with females and non-drivers making up most of this increase [9].

Most studies are in agreement that AVs are likely to increase VMT and congestion, due to increased trip making, the ability for AVs to search for more distant and cheaper parking, and the additional VMT generated from people switching from personally owned vehicles to shared autonomous vehicles, generating empty travel [5], [9], [14]. Current opportunities for future research in this area include: 1) simulating AVs considering a heterogeneous population of travelers with different values of travel time (VOTT) and 2) incorporating parking to estimate the impact of AVs on transportation system operations [15].

References

  1. H. Yan, K. M. Kockelman, and K. M. Gurumurthy, “Shared autonomous vehicle fleet performance: Impacts of trip densities and parking limitations,” Transp. Res. Part Transp. Environ., vol. 89, p. 102577, Dec. 2020, doi: 10.1016/j.trd.2020.102577.

  2. W. Zhang, S. Guhathakurta, and E. B. Khalil, “The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation,” Transp. Res. Part C Emerg. Technol., vol. 90, pp. 156–165, May 2018, doi: 10.1016/j.trc.2018.03.005.

  3. M. Hyland and H. S. Mahmassani, “Operational benefits and challenges of shared-ride automated mobility-on-demand services,” Transp. Res. Part Policy Pract., vol. 134, pp. 251–270, Apr. 2020, doi: 10.1016/j.tra.2020.02.017.

  4. S. Shafiei, Z. Gu, H. Grzybowska, and C. Cai, “Impact of self-parking autonomous vehicles on urban traffic congestion,” Transportation, vol. 50, no. 1, pp. 183–203, Feb. 2023, doi: 10.1007/s11116-021-10241-0.

  5. D. J. Fagnant and K. M. Kockelman, “The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios,” Transp. Res. Part C Emerg. Technol., vol. 40, pp. 1–13, Mar. 2014, doi: 10.1016/j.trc.2013.12.001.

  6. D. J. Fagnant and K. M. Kockelman, “Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas,” Transportation, vol. 45, no. 1, pp. 143–158, Jan. 2018, doi: 10.1007/s11116-016-9729-z.

  7. G. Gelauff, I. Ossokina, and C. Teulings, “Spatial and welfare effects of automated driving: Will cities grow, decline or both?,” Transp. Res. Part Policy Pract., vol. 121, pp. 277–294, Mar. 2019, doi: 10.1016/j.tra.2019.01.013.

  8. A. Millard-Ball, “The autonomous vehicle parking problem,” Transp. Policy, vol. 75, pp. 99–108, Mar. 2019, doi: 10.1016/j.tranpol.2019.01.003.

  9. C. D. Harper, C. T. Hendrickson, S. Mangones, and C. Samaras, “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., vol. 72, pp. 1–9, Nov. 2016, doi: 10.1016/j.trc.2016.09.003.

  10. Z. Wadud, D. MacKenzie, and P. Leiby, “Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles,” Transp. Res. Part Policy Pract., vol. 86, pp. 1–18, Apr. 2016, doi: 10.1016/j.tra.2015.12.001.

  11. A. Talebpour, H. S. Mahmassani, and A. Elfar, “Investigating the Effects of Reserved Lanes for Autonomous Vehicles on Congestion and Travel Time Reliability,” Transp. Res. Rec. J. Transp. Res. Board, no. 2622, 2017, Accessed: May 13, 2024. [Online]. Available: https://trid.trb.org/View/1438766

  12. R. E. Stern et al., “Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,” Transp. Res. Part C Emerg. Technol., vol. 89, pp. 205–221, Apr. 2018, doi: 10.1016/j.trc.2018.02.005.

  13. Y. Zhao and K. M. Kockelman, “Anticipating the Regional Impacts of Connected and Automated Vehicle Travel in Austin, Texas,” J. Urban Plan. Dev., vol. 144, no. 4, p. 04018032, Dec. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000463.

  14. C. D. Harper, C. T. Hendrickson, and C. Samaras, “Exploring the Economic, Environmental, and Travel Implications of Changes in Parking Choices due to Driverless Vehicles: An Agent-Based Simulation Approach,” J. Urban Plan. Dev., vol. 144, no. 4, p. 04018043, Dec. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000488.

  15. Md. M. Rahman and J.-C. Thill, “Impacts of connected and autonomous vehicles on urban transportation and environment: A comprehensive review,” Sustain. Cities Soc., vol. 96, p. 104649, Sep. 2023, doi: 10.1016/j.scs.2023.104649.

Related Literature Reviews

See Literature Reviews on Automated Vehicles

See Literature Reviews on Transportation Systems Operations

Note: Mobility COE research partners conducted this literature review in Spring of 2024 based on research available at the time. Unless otherwise noted, this content has not been updated to reflect newer research.