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Parking futures: Shared automated vehicles and parking demand reduction trajectories in Atlanta

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  • Zhang, Wenwen
  • Wang, Kaidi

Abstract

The coming of automated vehicles (AVs) and Mobility-on-Demand (MoD services) is expected to reduce urban parking demand and correspondingly alter the urban parking landscape in a significant way. Multiple modeling efforts have already demonstrated that Shared AVs (SAVs) have promising potential to decrease urban parking demand. However, previous studies have only examined SAV parking demand at one point in time, with various market penetrations. It remains unclear what the demand reduction trajectory will be like during the transition period when there is a mix of SAVs, Privately-Owned AVs (PAVs), Shared Conventional Vehicles (SCVs), and Conventional Private Vehicles (CPVs). This study fills this gap by developing an agent-based simulation model to examine the spatially and temporally explicit parking reduction trends with mixed travel modes from 2020–2040. The results indicate that in the most optimal AV and MoD adoption scenario, the parking demand will decrease by over 20% after 2030, especially in core urban areas. Meanwhile, the parking demand in residential zones may double, which could lead to transportation equity concerns. Additionally, parking relocation may also induce environmental issues by generating a considerable amount of empty Vehicle Miles Traveled (VMT). To reap the benefits brought by AVs and MoD systems and to mitigate the accompanying social and environmental issues, our results suggest that proactive policymakers in the next decade will need to modify land use regulations for both new developments and existing parking infrastructure in commercial and residential zones, as well as update travel demand management policies.

Suggested Citation

  • Zhang, Wenwen & Wang, Kaidi, 2020. "Parking futures: Shared automated vehicles and parking demand reduction trajectories in Atlanta," Land Use Policy, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:lauspo:v:91:y:2020:i:c:s0264837718314443
    DOI: 10.1016/j.landusepol.2019.04.024
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yilun Chen & Nirajan Shiwakoti & Peter Stasinopoulos & Shah Khalid Khan, 2022. "State-of-the-Art of Factors Affecting the Adoption of Automated Vehicles," Sustainability, MDPI, vol. 14(11), pages 1-29, May.
    2. Chenhao Zhu & Jonah Susskind & Mario Giampieri & Hazel Backus O’Neil & Alan M. Berger, 2023. "Optimizing Sustainable Suburban Expansion with Autonomous Mobility through a Parametric Design Framework," Land, MDPI, vol. 12(9), pages 1-31, September.
    3. Chai, Huajun & Rodier, Caroline J. & Song, Jeffery W. & Zhang, Michael H. & Jaller, Miguel, 2023. "The impacts of automated vehicles on Center city parking," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    4. Chi Feng & Zhenyu Mei, 2023. "Optimization of Shared Autonomous Vehicles Routing Problem: From the View of Parking," Sustainability, MDPI, vol. 15(16), pages 1-17, August.
    5. Winter, Konstanze & Cats, Oded & Martens, Karel & van Arem, Bart, 2021. "Parking space for shared automated vehicles: How less can be more," Transportation Research Part A: Policy and Practice, Elsevier, vol. 143(C), pages 61-77.
    6. Hongjun Cui & Mingzhi Li & Minqing Zhu & Xinwei Ma, 2023. "Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model," Sustainability, MDPI, vol. 15(15), pages 1-22, August.
    7. You Kong & Jihong Ou & Longfei Chen & Fengchun Yang & Bo Yu, 2023. "The Environmental Impacts of Automated Vehicles on Parking: A Systematic Review," Sustainability, MDPI, vol. 15(20), pages 1-21, October.

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