Impact of Connected and Automated Vehicles (CAVs) on Freeway Capacity

Fan, Wei (David); Liu, Pengfei · 2019 · ROSA P / University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education

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Summary

This study addresses the critical gap in transportation planning regarding the evaluation of freeway capacity in the presence of Connected and Automated Vehicles (CAVs). Current methodologies, such as those in the Highway Capacity Manual (HCM), fail to account for CAV-specific strategies like reduced headways, narrower lane widths, and coordinated maneuvers. As CAV market penetration increases, there is a pressing need to quantify their impact on roadway capacity to inform future infrastructure planning and traffic management. The research aims to develop guidelines for adjusting HCM capacity estimates to reflect varying levels of CAV and Autonomous Vehicle (AV) penetration. To achieve this, the authors employed a simulation-based approach using VISSIM, a microscopic traffic simulation tool. The study selected four distinct freeway scenarios from the Caltrans Performance Measurement System (PeMS): a basic freeway segment, an on-ramp segment, an off-ramp segment, and a weaving segment. To ensure accuracy, the simulation model was calibrated to real-world traffic conditions for human-driven vehicles using a Genetic Algorithm to optimize driving behavior parameters, such as standstill distance and minimum headway. Because VISSIM’s internal driver model cannot simulate CAV operations, the researchers implemented an External Driver Behavior Model (EDBM) to define the specific driving characteristics of CAVs and AVs within the mixed-traffic environment. The numerical results analyzed the capacity tendencies across the four scenarios under various speed limits (80, 90, 104, and 120 km/h) and different combinations of regular vehicles, AVs, and CAVs. The study demonstrates that CAVs significantly influence freeway capacity by allowing for shorter headways and more efficient maneuvers, particularly in complex areas like weaving sections. The simulation quantifies how different market penetration levels of CAVs and AVs alter traffic flow and capacity, providing specific data on capacity adjustments required for mixed-traffic environments. The significance of this work lies in its provision of actionable guidelines for traffic engineers and stakeholders to estimate and predict freeway capacity as CAV technologies become more prevalent. By establishing a framework for HCM capacity adjustments, the study helps bridge the gap between current infrastructure standards and future transportation realities. The findings support better preparedness for CAV planning and operations, enabling more effective traffic management and infrastructure design that accounts for the mobility, safety, and capacity benefits of automated and connected vehicle technologies.

Key finding

CAV technologies increase freeway capacity by allowing smaller lane widths and reduced headways, with specific capacity improvements observed in weaving sections due to coordinated maneuvers.

Methodology

modeling

Provenance

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

Topics

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• traffic density