This study proposes a mixed traffic simulation framework that integrates vehicle car-following (CF) and lane-changing (LC) with connected and automated vehicles (CAVs) of different cooperation behaviors. This framework is centered at a CAV LC model incorporated with CF dynamics in mixed traffic. The model was calibrated and validated using data collected from small-scale field experiments in a previous study. To demonstrate a large-scale application of this framework, PTV Vissim was used to implement the framework on a segment of Interstate 75 highway. Sensitivity analyses were conducted to investigate the impacts of key parameters on traffic mobility and stability performance. The results show that traffic performance degraded as the traffic demand and vehicle diverging rate increased. As the CAV penetration rate increased, traffic performance fluctuated when CAVs were more conservative. As the CAV cooperation rate, incentive criterion threshold, and incentive criterion bias increased, mobility and stability performance first improved and then degraded. When CAV platooning was considered, traffic performance was enhanced. These findings shed light on mixed traffic management from the perspectives of both transportation operators (e.g., facilities and policies to promote vehicle cooperation) and automakers (e.g., tuning parameters in their LC models) to achieve the best traffic performance.

中文翻译：

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协同换道的混合交通仿真

本研究提出了一种混合交通仿真框架，该框架将车辆跟车 (CF) 和变道 (LC) 与具有不同合作行为的联网和自动驾驶车辆 (CAV) 集成在一起。该框架以 CAV LC 模型为中心，该模型与混合流量中的 CF 动力学相结合。使用先前研究中从小规模现场实验收集的数据对模型进行了校准和验证。为了演示该框架的大规模应用，PTV Vissim 被用于在 75 号州际公路的一段上实施该框架。进行敏感性分析以研究关键参数对交通流动性和稳定性性能的影响。结果表明，随着交通需求和车辆分流率的增加，交通性能下降。随着CAV渗透率的提高，当 CAV 更保守时，交通性能会波动。随着CAV合作率、激励准则阈值和激励准则偏差的增加，移动性和稳定性性能先提高后降低。当考虑 CAV 编队行驶时，交通性能得到提高。这些发现从运输运营商（例如，促进车辆合作的设施和政策）和汽车制造商（例如，调整其 LC 模型中的参数）的角度阐明了混合交通管理，以实现最佳交通性能。