The Rebound Effect of Autonomous Vehicles on Vehicle Miles Traveled: A Synthesis of Drivers, Impacts, and Policy Implications

Kyoungho Ahn (), Hesham A. Rakha and Jinghui Wang
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Kyoungho Ahn: Virginia Tech Transportation Institute, Blacksburg, VA 24061, USA
Hesham A. Rakha: Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Jinghui Wang: Aramco Research Center-Detroit, Aramco Americas, Novi, MI 48377, USA

Sustainability, 2025, vol. 17, issue 22, 1-27

Abstract: Autonomous vehicles (AVs), including privately owned self-driving cars and shared autonomous vehicles (SAVs), hold great potential to transform urban mobility by enhancing safety, accessibility, efficiency, and sustainability. However, their widespread deployment also carries the risk of significantly increasing vehicle miles traveled (VMT), a phenomenon known as the rebound effect. This paper examines the VMT rebound effects resulting from AV and SAV deployment, drawing on recent studies and global case insights. We conducted a systematic narrative review of 48 studies published between 2019 and 2025, drawing on academic sources and credible agency reports. We do not conduct a meta analysis. We quantify how different automation levels (SAE Levels 3, 4, 5) impact VMT and identify the primary factors driving VMT growth, namely: reduced perceived travel time cost, induced demand from new user groups, modal shifts away from transit, and empty VMT. Global case studies from North America, Europe, Asia, and the Middle East are reviewed alongside regional policy responses. Quantitative analyses indicate moderate to significant VMT increases under most scenarios—for example, approximately 10 to 20% increases with conditional automation and potentially over 50% with high/full automation, under the circumstances of no effective policy interventions. Meanwhile, aggressive ride-sharing and policy interventions, including road pricing and transit integration, can mitigate or even reverse these increases. The discussion provides a critical assessment of policy strategies such as mileage pricing, SAV incentives, and integrated land-use/transport planning to manage VMT growth. We conclude that without proactive policies, widespread AV adoption is likely to induce a rise in VMT, but that a suite of well-designed measures can steer automated mobility towards sustainable outcomes. These findings help policymakers and planners balance AV benefits with congestion, energy use, and climate goals.

Keywords: autonomous vehicle; AV; shared autonomous vehicle; SAV; VMT; rebound effect (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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