An assessment of autonomous vehicles : traffic impacts and infrastructure needs : final report.

Kockelman, Kara; Boyles, Stephen; Stone, Peter; Fagnant, Dan; Patel, Rahul; Levin, Michael W.; Sharon, Guni; Simoni, Michele; Albert, Michael; Fritz, Hagen; Hutchinson, Rebecca; Bansal, Prateek; Domnenko, Gleb; Bujanovic, Pavle; Kim, Bumsik; Pourrahmani, Elham; Agrawal, Sudesh; Li, Tianxin; Hanna, Josiah; Nichols, Aqshems; Li, Jia · 2017 · ROSA P / University of Texas at Austin. Center for Transportation Research

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Summary

This report, commissioned by the Texas Department of Transportation and conducted by the University of Texas at Austin, assesses the traffic impacts and infrastructure needs associated with connected and autonomous vehicles (CAVs). The study aims to synthesize contemporary smart driving technologies, forecast their adoption rates, and quantify their potential effects on safety, congestion, and mobility in Texas. The research utilizes the National Highway Traffic Safety Administration’s four-level taxonomy to classify automation levels, identifying that while Level 0 and 1 technologies are mainstream, Levels 3 and 4 face significant barriers regarding cost, reliability, and legislation. To forecast adoption, the researchers conducted a Texas-wide survey of 1,364 respondents to gauge public perception, willingness to pay (WTP), and technology preferences. These data informed a simulation-based fleet evolution framework projecting adoption from 2015 to 2045 under varying annual price-reduction scenarios (1%, 5%, and 10%). The study found that advanced automation is currently unpopular; over half of respondents were unwilling to pay for Level 3 or 4 features. Under a 10% annual price-reduction scenario, blind-spot monitoring and emergency braking showed the highest projected adoption (59.4%), while full self-driving (Level 4) reached only 38.5% and limited self-driving (Level 3) just 16.9%. The analysis suggests that without policy mandates or rapid cost reductions, the vehicle fleet will remain heterogeneous through 2045. The study modeled traffic impacts using static four-step planning and dynamic traffic assignment (DTA) simulations, incorporating multi-class cell transmission models and conflict region modeling for intersections. These models simulated varying proportions of CAVs and human-driven vehicles on Texas roadways. Key findings indicate that CAVs will likely increase vehicle miles traveled (VMT) by reducing the perceived burden of travel time, enabling longer trips and facilitating mobility for those unable to drive. However, CAVs are projected to significantly improve safety, potentially saving over 2,400 lives annually in Texas at 90% market penetration. This would result in more than $62 billion in comprehensive crash cost savings, representing a 75% reduction. Additionally, the net present value of privately owned CAVs is estimated at nearly $14,000 per vehicle at 10% penetration, rising to $27,000 at 90% penetration, driven by productivity gains and reduced crash costs. The significance of this work lies in its comprehensive economic and operational assessment of CAV integration. It highlights that while CAVs offer substantial safety and economic benefits, they may exacerbate congestion through increased VMT unless managed through shared autonomous vehicle (SAV) frameworks or infrastructure adjustments. The report provides specific monetary estimates and adoption forecasts to guide transportation planning and policy development, emphasizing the need for infrastructure upgrades and regulatory frameworks to support the transition toward automated mobility.

Key finding

CAV market penetration of 90% could save over 2,400 lives annually in Texas and generate more than $14 billion in economic savings from crash reductions.

Methodology

mixed_methods

Sample size: 1364

Provenance

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

Topics

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• acceptance adoption
• traffic density
• driverless ads
• situational awareness
• exposure measurement

Information type

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• Empirical Findings: observational prevalence
• Theoretical Contribution: computational model, conceptual framework