# Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways
## 2 Literature Review  
A considerable amount of research and investigation has been carried out on mixed traffic flow [11; 12]. These studies include the analysis of interactions among various types of vehicles, the development and validation of traffic flow models, the implementation of multi-lane and/or different scenarios, and the optimization of traffic management strategies to improve traffic performance [13; 14; 15]. In the case of fully or partially automated vehicles coexisting with manual vehicles, the proposed optimized control strategy can achieve maximum throughput of traffic flow by minimizing (or even preventing) congestion [16]. In the mixed traffic flow where CAVs coexist with HDVs, a hyperbolic system based on partial differential equations has been proposed to capture the macroscopic traffic flow dynamics [17]. A cellular automata model-based traffic system was proposed to investigate the characteristics of mixed traffic modes involving both autonomous and human-driven vehicles [18]. In this model, human drivers can use information from several preceding vehicles to guide their vehicle's motion, while autonomous vehicles consider communication ranges between vehicles and iterative interaction information. In the mixed traffic flow of connected, autonomous, and human-driven vehicles, a second-order macroscopic model was developed to reconstruct the backward propagation speed of disturbances based on the dynamic headway of the vehicles [19]. This research utilized a data-driven optimization method to model the driving behavior of CAVs in mixed traffic and explored the impact of CAVs on overall traffic performance [20]. A traffic control system for signalized intersections based on deep reinforcement learning was proposed to enhance the operational efficiency and safety of CAVs and HDVs while minimizing the impact of HDVs' driving behaviors [21]. This study explored the necessity for a comprehensive connected and automated vehicle simulation environment to simulate vehicle behavior, taking into account the influence of surrounding objects on machine perception and signal propagation [22]. This research analyzed the effects of the coexistence of conditionally automated vehicles (CADVs) and human drivers on the stability of mixed traffic flow [23]. The results showed that the switch control from CADVs to human drivers greatly increased the instability of mixed traffic flow. To improve the safety and efficiency of dynamic interaction merging zones with multiple CAVs, a method known as graph with multi-head attention and deep reinforcement learning (GMA-DRL) has been proposed [24]. The findings showed that the proposed method is superior to the existing cooperative control models in headway distance, average speed, and acceleration, thereby improving traffic safety and efficiency. By modifying the four factors of traffic flow, truck proportion, cooperative adaptive cruise control (CACC) market penetration rate, and CACC platoon size, this study analyzed the impact of these factors on the characteristics of mixed traffic flow composed of HDVs and heterogeneous CACC platoons in highway merging zones [25]. To minimize disruptions from incorrect driver actions and avoid unnecessary platoon operations, a framework that integrates driving state recognition, platoon control, and risk assessment has been suggested. The findings demonstrated that the proposed method significantly enhanced the stability and safety of the multi-platoon system [26]. The current state and future outlook of the CACC system are summarized based on communication, driver characteristics, and control [27]. To assess the safety and mobility of CACC, a simulation platform for urban scenarios has been proposed to capture the interactions between HDVs and CAVs [28]. A new set of lane-changing regulations has been implemented to model multi-lane heterogeneous traffic that includes human-driven vehicles, automated vehicles, and communicating automated vehicles [29].