Developing Analysis, Modeling, and Simulation Tools for Connected and Automated Vehicle Applications

Huang, Zhitong; Hale, David K.; Shladover, Steven E.; Lu, Xiao-Yun; Liu, Hao; Li, Qianwen; Li, Xiaopeng; Mahmassani, Hani; Talebpour, Alireza; Hosseini, Moein; Elfar, Amr · 2021 · ROSA P / United States. Federal Highway Administration. Office of Operations Research and Development

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Summary

This report addresses the inadequacy of existing traffic analysis, modeling, and simulation (AMS) tools for evaluating connected and automated vehicle (CAV) applications. While CAVs promise significant mobility, safety, and environmental benefits, current AMS tools lack the capability to represent vehicle connectivity, communication, and automated driving behaviors. This limitation has led to divergent assumptions in the literature and a lack of consensus regarding CAV impacts. The Federal Highway Administration sponsored this project to develop consistent, data-driven AMS models for prominent CAV applications and integrate them into existing simulation tools to improve the state of practice. The study focused on four specific CAV applications: cooperative adaptive cruise control (CACC), automated lane changing (LC), speed harmonization (SPDHRM), and merge coordination. Researchers developed detailed logic and mathematical models for each application, incorporating variables such as vehicle states, incentive criteria, and safety distances. These models were calibrated and validated using real-world datasets, including trajectory data collected on Interstate 35 near Austin, Texas, and variable speed advisory (VSA) data. The development process involved defining model logic, calibrating parameters to minimize error metrics like root mean square error (RMSE), and validating against independent datasets using statistical tests such as the Kolmogorov-Smirnov test. The finalized models were then implemented into traffic-simulation tools to conduct sensitivity studies and case analyses. Simulation results demonstrated the impact of CAV market penetration rates and driver compliance levels on traffic flow and fuel efficiency. For CACC applications, simulations showed that higher market penetration and full driver compliance with variable speed advisories improved average vehicle speeds and fuel efficiency, particularly in congested conditions. Automated lane changing models revealed that CAV cooperation rates and incentive criteria significantly influenced traffic stability under both congested and uncongested scenarios. The joint application of speed harmonization and merge coordination showed that centralized and optimization-based strategies could enhance traffic throughput and reduce speed variance compared to decentralized approaches or baseline manually driven vehicle scenarios. Fundamental diagrams generated from simulations illustrated shifts in capacity and stability thresholds as CAV penetration increased. The significance of this work lies in providing a standardized, validated framework for simulating CAV impacts, addressing the previous lack of consensus in the field. By integrating these models into existing AMS tools, the report enables transportation agencies and researchers to more accurately estimate the benefits of CAV technologies before deployment. The findings support the development of deployment strategies and highlight the importance of driver compliance and cooperation rates in realizing CAV benefits. The report concludes with recommendations for future research, including addressing model limitations and expanding the scope of applications to further refine AMS capabilities for emerging transportation technologies.

Key finding

The developed analysis, modeling, and simulation tools successfully integrate cooperative adaptive cruise control, automated lane changing, speed harmonization, and merge coordination models into existing simulation frameworks to evaluate CAV impacts.

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).

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

Topics

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• traffic density
• situational awareness
• speed choice

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).

• Methodological Resource: tool software, validation psychometrics
• Theoretical Contribution: computational model