Assessment of Capacity Changes Due to Automated Vehicles on Interstate Corridors
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Summary
This study assesses the impact of automated vehicles (AVs) and connected automated vehicles (CAVs) on the capacity of Virginia interstate corridors. Motivated by the Virginia Department of Transportation’s goal to ensure efficient highway operations and address congestion, the research addresses the lack of calibrated models for computer-driven vehicles in traditional traffic simulation software. The primary objective was to quantify capacity changes under varying levels of market penetration and roadway characteristics, providing guidance for future planning and design guidelines. The researchers employed PTV VISSIM traffic simulation software to evaluate three vehicle types: legacy vehicles (LVs), AVs equipped with adaptive cruise control (ACC), and CAVs equipped with cooperative adaptive cruise control (CACC). The experimental design included mixed traffic scenarios involving light-duty passenger vehicles and heavy vehicles (HVs). Simulations were conducted on three test geometries: a basic freeway segment, a model of Interstate 95 (I-95), and a model of Interstate 81 (I-81) to account for extended grades and high HV percentages. The study also reviewed legal and regulatory frameworks, including following distance standards and the Assured Clear Distance Ahead doctrine, to define permissible vehicle behaviors. Results indicated that 100% AV and 100% CAV scenarios increased road capacity by 28% and 92%, respectively, compared to 100% LV scenarios on a basic freeway segment. CAVs demonstrated superior performance due to their ability to communicate and increase string stability, which mitigated the stop-and-go conditions and reduced speeds observed in AV-only scenarios during congestion. Under uncongested conditions, both AVs and CAVs improved throughput and reduced delays, though they caused slight decreases in speed. On I-81, steep grades negatively affected all vehicle types, particularly HVs, but CAVs at high market penetrations still achieved significant capacity increases over AV and LV scenarios. The study concluded that while full market penetration is required for maximum potential, AVs and CAVs offer operational benefits even in mixed traffic. The findings imply that VDOT should update simulation models to reflect anticipated vehicle fleets and use the reported capacity estimates for calibrating freeway corridor simulations. The authors recommend investigating methods to estimate the prevalence and usage rates of CV and AV technologies. Additionally, the legal review suggests that existing statutes referring to human "drivers" may not apply to fully automated systems, potentially allowing for shorter following distances and further capacity gains. The study highlights the need for continued monitoring of AV developments to ensure infrastructure planning aligns with technological advancements.
Key finding
Full market penetration of connected automated vehicles increased road capacity by 92 percent over legacy vehicles, while automated vehicles increased capacity by 28 percent.
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.
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- Theoretical Contribution: computational model