Travel Effects and Associated Greenhouse Gas Emissions of Automated Vehicles

Rodier, Caroline Jane · 2018 · ROSA P / National Center for Sustainable Transportation

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Summary

This 2018 white paper by Caroline Rodier, published by the National Center for Sustainable Transportation, critically reviews existing literature on the travel effects and greenhouse gas (GHG) emissions associated with automated vehicles (AVs). The study addresses the uncertainty surrounding AV impacts, noting that while experts predict full automation between 2025 and 2035, fully automated vehicles have not yet entered widespread use. Consequently, the paper synthesizes theoretical models, extrapolations from current behavior, and scenario modeling to estimate how AVs might alter vehicle miles traveled (VMT), energy consumption, and land use patterns. The analysis examines seven primary mechanisms driving changes in travel demand: increased roadway capacity, reduced travel time burden, changes in monetary costs, parking and relocation travel, induced travel, new traveler groups, and mode choice. The paper reviews microsimulation studies, stated preference surveys, and elasticity estimates from existing transportation literature. For instance, it evaluates how reduced headways could double or triple road capacity, how the ability to work or relax during travel might lower the value of travel time, and how lower insurance and fuel costs could reduce per-mile expenses. The review also incorporates scenario modeling studies that simulate fleets of personal AVs and automated taxis (with and without sharing) in cities such as Austin, Atlanta, Berlin, and Lisbon. Key findings indicate that personal automated vehicles and automated taxis are likely to significantly increase VMT and GHG emissions due to induced travel, empty relocation trips, and the entry of new travelers (e.g., the elderly, disabled, and youth). For example, studies estimate VMT increases of 10% to 14% from new travelers alone. While shared automated taxis could reduce VMT and GHGs by up to 22%, the paper notes that pricing policies are likely necessary to encourage ride-sharing. Additionally, AVs could reduce parking demand by approximately 90%, potentially freeing up urban land but exacerbating congestion through empty vehicle relocation. The evidence quality varies; while induced travel elasticities are well-established, estimates for monetary cost reductions and empty relocation travel remain highly speculative or limited to specific urban simulations. The paper concludes that without intervention, AV adoption may lead to increased congestion, urban sprawl, and higher emissions, particularly if the fleet is not electrified. To mitigate these negative effects, the authors recommend policies such as reinvesting in heavy rail transit with AV shuttles for first- and last-mile access, and implementing cordon pricing in city centers to manage congestion and prevent suburban sprawl. The study highlights the need for more measured data and robust modeling to accurately predict the long-term environmental and societal impacts of automated transportation systems.

Key finding

Personal automated vehicles and non-shared automated taxis are likely to significantly increase vehicle miles traveled and greenhouse gas emissions, whereas shared automated taxi fleets could substantially reduce these metrics if supported by appropriate pricing policies.

Methodology

review

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• acceptance adoption
• driverless ads