In the foreseeable future, connected vehicles (CVs) will coexist with traditional vehicles (TVs) resulting in a complex mixed traffic environment and the success of CVs will depend on the characteristics of this mixed traffic. Therefore, before the large scale deployment of CVs, it is important to examine how CVs will influence the characteristics of the resultant mixed traffic. To achieve this aim, this study models the mixed traffic of TVs and CVs, and examines the traffic flow disturbance, efficiency, and safety. Intelligent Driver Model (IDM) with estimation errors is utilised to model TVs since it incorporates human factors such as estimation errors. Whereas, connected vehicle driving strategy integrated with IDM is utilised to model CVs because it incorporates driver compliance, a critical human factor for the success of CVs. Moreover, two classes of drivers based on their compliance levels are considered, namely the high-compliance drivers and the low-compliance drivers, to comprehensively investigate the impact of driver compliance on the mixed traffic of CVs and TVs. Two simulation experiments are performed in this study. The first experiment is used to measure traffic flow disturbance and safety while the second is used to measure the traffic flow efficiency. Furthermore, a total of 7 mixed traffic environments are generated in each experiment via different combinations of TVs, CVs with low compliance drivers, and CVs with high compliance drivers. Another important point considered in the simulation is the spatially distribution of CVs in the platoon. As such, three platoon policies are investigated. In the first policy i.e., the best case, the CVs are spatially arranged with a motive to maximise benefits from CVs whereas in the second policy i.e., the worst case, the CVs are spatially arranged with a motive to minimise benefits from CVs. Finally, in the third platoon policy i.e., the random case, the CVs are distributed randomly in the platoon. This study demonstrates the importance of the spatial arrangement of CVs in a platoon at a given penetration rate and its impact on traffic flow disturbance, efficiency, and safety. Moreover, findings from this study underscores that CVs can supress the traffic flow disturbance, and enhance traffic flow efficiency, and safety; however, traffic engineers and policy makers have to be cautious regarding how CVs are distributed in a traffic stream when deploying these vehicles in the real world traffic environment.

在可预见的将来，联网车辆（CV）将与传统车辆（TV）共存，从而形成复杂的混合交通环境，而CV的成功将取决于这种混合交通的特征。因此，在大规模部署CV之前，重要的是检查CV将如何影响最终混合流量的特性。为了实现此目标，本研究对电视和CV的混合流量进行建模，并检查流量干扰，效率和安全性。具有估计误差的智能驾驶员模型（IDM）可用于对电视进行建模，因为它结合了诸如估计误差之类的人为因素。鉴于与IDM集成的互联车辆驾驶策略可用于对CV建模，因为它融合了驾驶员的依从性，这是CV成功的关键人为因素。此外，根据合规性级别考虑了两类驱动程序，即高合规性驱动程序和低合规性驱动程序，以全面调查驾驶员合规性对CV和电视混合流量的影响。在这项研究中进行了两个模拟实验。第一个实验用于测量交通流干扰和安全性，第二个实验用于测量交通流效率。此外，在每个实验中，通过电视，具有低合规性驱动程序的CV和具有高合规性驱动程序的CV的不同组合，总共生成了7种混合交通环境。模拟中考虑的另一个重要点是排中CV的空间分布。因此，研究了三种排政策。在第一个政策（即最好的情况）下，CV在空间上的动机是最大化CV的利益，而在第二种策略（即最坏的情况）下，CV在空间上的动机是最小化CV的利益。最后，在第三个排策略中，即随机情况下，CV在排中随机分布。这项研究证明了在给定的穿透率下，排中CV的空间布置的重要性及其对交通流干扰，效率和安全性的影响。此外，这项研究的结果强调，CV可以抑制交通流干扰，并提高交通流效率和安全性。但是，在实际交通环境中部署这些车辆时，交通工程师和政策制定者必须谨慎对待CV在交通流中的分配方式。