Developing Analysis, Modeling, and Simulation Tools for Connected and Automated Vehicle Applications: A Case Study for I-66 in Virginia

Ma, Jiaqi; Guo, Yi; Huang, Zhitong · 2021 · ROSA P / United States. Federal Highway Administration

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Summary

This report documents a simulation-based case study investigating the effectiveness of SAE J3016 Level 1 connected and automated vehicle (CAV) technologies in mitigating congestion, fuel consumption, and emissions. Motivated by the need to quantify real-world benefits of CAVs before high market penetration occurs, the study focused on Interstate 66 (I-66) in Northern Virginia, a congested corridor managed by the Virginia Department of Transportation. The research aimed to evaluate three specific CAV applications—cooperative adaptive cruise control (CACC), speed harmonization, and cooperative merge—and assess their impact when combined with infrastructure enhancements, specifically managed lane (ML) concepts involving dedicated ramps and CV/CAV-eligible high-occupancy vehicle lanes. The methodology utilized a microscopic simulation framework based on a calibrated 2015 network of a 13-mile segment of I-66. The team developed external driver models to represent CAV behaviors, including CACC platooning logic, trajectory-based speed harmonization, and cooperative merging algorithms. Experimental design varied critical parameters such as CAV market penetration rates, traffic demand levels (up to 130 percent of baseline), and infrastructure configurations. Three ML scenarios were tested: ML 1 (dedicated ramps and CV/CAV-eligible HOV lanes), ML 2 (CV/CAV-eligible HOV lanes only), and ML 3 (standard HOV lanes as baseline). Performance was measured using throughput, delay, and capacity metrics. Results indicated that both individual and bundled CAV applications significantly improved traffic performance. CACC was identified as the most effective individual strategy, directly reducing vehicle gaps and stabilizing flow; at 100 percent market penetration, CACC increased pipeline capacity by 81.2 percent. Speed harmonization smoothed mainline traffic, increasing throughput and reducing delay, while cooperative merge reduced forced merges by having mainline vehicles adjust speeds or lanes to create safe gaps. Crucially, bundled CAV applications at high penetration rates enabled the corridor to handle 130 percent of traffic demand without congestion. Infrastructure analysis revealed that ML 1 performed best in low-to-medium penetration scenarios, demonstrating that dedicated ramps and operational strategies provide benefits even during early deployment stages. At high penetration levels, all infrastructure scenarios performed similarly due to the robustness of the bundled CAV applications. The study concludes that CAV technologies offer substantial mobility benefits even at low market penetration rates, supporting early deployment strategies. The findings suggest that existing HOV facilities can leverage CAV technologies with minimal infrastructure adjustments to realize immediate system benefits. These results provide state and local transportation departments with operational insights for strategic planning, highlighting the potential for CAVs to enhance capacity and safety on existing roadways without requiring extensive new construction.

Key finding

Bundled CAV applications with high market penetration rates can handle 130 percent of traffic demand without congestion, while cooperative adaptive cruise control strings provide the most significant individual capacity increase.

Methodology

simulator

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).

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

Topics

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• traffic density
• v2x connected vehicle

Information type

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• Methodological Resource: tool software
• Theoretical Contribution: computational model