Benefits estimation framework for automated vehicle operations.
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Summary
This report presents a comprehensive modeling framework designed to estimate the potential benefits and dis-benefits of automated vehicle (AV) operations within the U.S. surface transportation system. The research addresses the need for a rigorous quantitative assessment tool to inform federal policy and long-range planning, given that AVs promise transformative improvements in safety, mobility, energy efficiency, and accessibility. However, these technologies also introduce complex second-order impacts, such as potential increases in vehicle-miles traveled (VMT) and changes in land use, which require careful evaluation to ensure net positive outcomes. The framework employs a multi-submodel approach that integrates existing analytical tools to capture impacts across multiple time scales, from second-by-second vehicle performance to multi-year regional trends. It is structured to compare various automation scenarios, including different levels of automation (from driver assistance to full automation) and degrees of vehicle-to-infrastructure connectivity. The core components include: (1) Safety, utilizing the Safety Impact Methodology (SIM) to analyze crash exposure, prevention, and severity based on historical data from sources like the National Automotive Sampling System; (2) Vehicle and Regional Mobility, using microsimulation for car-following dynamics and meso-scale models for corridor and intersection performance; (3) Energy and Environment, linking mobility-derived driving cycles to the EPA’s Motor Vehicle Emissions Simulator (MOVES2014) to calculate fuel consumption and tailpipe emissions; (4) Transportation System Usage, modeling traveler responses to changes in mobility and accessibility; (5) Accessibility, measuring the ability to reach destinations; (6) Land Use, assessing long-term development patterns; and (7) Economic Analysis, quantifying macro-economic impacts. The report details the specific metrics and data sources for each domain. For safety, it proposes measures such as crash rates normalized by population and VMT, alongside monetized crash values. Mobility metrics include lane capacity and travel time reliability. The framework is designed to be built incrementally, starting with safety and mobility models to validate results against existing research on lower-level automation applications like cooperative adaptive cruise control. It accounts for mixed vehicle streams and varying market penetration rates to reflect real-world adoption scenarios. The significance of this work lies in its provision of a standardized, quantitative tool for policymakers, state departments of transportation, and metropolitan planning organizations. By enabling the comparison of diverse automation futures, the framework helps identify which technologies yield sufficient benefits to warrant encouragement and which pose risks requiring mitigation. It supports evidence-based decision-making regarding infrastructure investments, regulatory policies, and the integration of AVs into the broader transportation network, ensuring that potential dis-benefits are adequately captured alongside projected gains.
Key finding
The report establishes a structured, multi-submodel framework for quantifying the wide-ranging impacts of automated vehicle operations, rather than providing specific empirical results from a single experiment.
Methodology
modeling
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- driverless ads
- situational awareness
- acceptance adoption
- exposure measurement
- passenger motion sickness comfort
- traffic density
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: crash risk outcomes
- Theoretical Contribution: computational model, conceptual framework