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<p>Mixed traffic flow, consisting of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs), is expected to dominate future roadways. This study develops a heterogeneous traffic flow model in an intelligent connected environment to analyze macroscopic characteristics, including traffic capacity, critical density, and fundamental diagrams, under varying CAV penetration rates. Three car-following modes are considered: HDVs following HDVs, HDVs following CAVs, and CAVs following CAVs, modeled respectively by the Intelligent Driver Model (IDM), Adaptive Cruise Control (ACC), and Cooperative Adaptive Cruise Control (CACC). Model parameters are calibrated using genetic algorithms based on publicly available autonomous driving datasets. Calibration results demonstrate high predictive accuracy, with mean absolute percentage errors of 0.009% (distance) and 2.37% (speed) for IDM, 0.27% and 3.67% for ACC, and near-zero errors for CACC. Analysis of fundamental diagrams shows that increasing CAV penetration significantly enhances traffic flow efficiency, improves stability, and mitigates congestion. The findings provide theoretical guidance for optimizing mixed traffic operations and support the development of intelligent and connected transportation systems.</p>

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Evaluating the characteristics of mixed traffic flow under the intelligent connected environment based on autonomous driving dataset

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Ying Hu,
  • Xiaonian Shan,
  • Changxin Wan

摘要

Mixed traffic flow, consisting of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs), is expected to dominate future roadways. This study develops a heterogeneous traffic flow model in an intelligent connected environment to analyze macroscopic characteristics, including traffic capacity, critical density, and fundamental diagrams, under varying CAV penetration rates. Three car-following modes are considered: HDVs following HDVs, HDVs following CAVs, and CAVs following CAVs, modeled respectively by the Intelligent Driver Model (IDM), Adaptive Cruise Control (ACC), and Cooperative Adaptive Cruise Control (CACC). Model parameters are calibrated using genetic algorithms based on publicly available autonomous driving datasets. Calibration results demonstrate high predictive accuracy, with mean absolute percentage errors of 0.009% (distance) and 2.37% (speed) for IDM, 0.27% and 3.67% for ACC, and near-zero errors for CACC. Analysis of fundamental diagrams shows that increasing CAV penetration significantly enhances traffic flow efficiency, improves stability, and mitigates congestion. The findings provide theoretical guidance for optimizing mixed traffic operations and support the development of intelligent and connected transportation systems.