# Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways
## 2 Literature Review
The influence of AVs on efficiency and safety was evaluated through average speed, throughput, and inverse time-to-collision (ITTC) in the case of mixed traffic and AV-only lanes [30]. A cooperative mandatory platoon lane change (CMPLC) strategy has been proposed for dedicated lanes (DLs) and general-purpose lanes (GPLs) in mixed traffic involving both CAVs and HDVs. The results demonstrated that the proposed strategy achieved higher lane change success rates and shorter execution times under different traffic demands, platoon sizes, and starting positions [31]. To enhance traffic efficiency in bottleneck zones involving mandatory lane changes, this study introduced a collision risk mitigation strategy using a multi-agent deep reinforcement learning framework. This approach took into account vehicle spacing and coordinated lane changes in a CAV environment to effectively alleviate traffic congestion [32]. To enhance traffic capacity, minimize delays, and smooth traffic flow, a multiclass multi-lane Cell Transmission Model (CTM) has been developed to simulate the traffic dynamics of mixed CAVs and HDVs [33]. A reservation-based cooperative eco-driving model (RCEM) has been developed to improve the CAV trajectories and management of intersection controls in situations with isolated intersections and different market penetration rates of CAVs [34]. A dynamic programming method and a mixed-integer linear programming (MILP) formulation have been proposed to optimize intersection management for mixed traffic flow that includes both CAVs and HDVs [35]. In a typical isolated ring with mixed traffic conditions, simulations were carried out with various configurations of CAV platoon [36]. The findings showed that increasing the maximum platoon size and platoon willingness can significantly enhance throughput and reduce delay times. Additionally, a heterogeneous platoon was better than a homogeneous platoon under different scenarios. Within the cooperative, connected, and automated Mobility (CCAM) framework, a simulation was conducted to study the coexistence of connected and automated vehicles and human-driven cars at a roundabout [37]. To investigate how cooperative and autonomous communication vehicles with platoon capabilities can prevent stop-and-go waves in urban environments, experimental simulations were carried out in SUMO (Simulation of Urban MObility) [38]. A ramp merging strategy based on game theory has been proposed to address the optimal merging coordination of CAVs under mixed traffic conditions [39]. The experiment, considering different penetration rates and congestion levels, was simulated on the Unity-SUMO integrated platform. The open-source platooning simulator PERMIT based on SUMO [40; 41; 42] and its extension Flexe were designed to simulate platooning maneuvers in mixed traffic situations involving both autonomous and non-autonomous vehicles [43]. Existing research mainly concentrates on traffic flow simulation for individual vehicle types, while there are relatively few studies on the interactions of multiple vehicle types in complex mixed traffic flow. To assess the impact of heterogeneous traffic flow on traffic efficiency, dynamics, safety, and environment, different penetration rates were considered on both the multi-lane ring road and highway. The mixed traffic consists of cars and trucks equipped with CAVs, AVs, and HDVs. All experiments utilized an integrated SUMO and Python simulation platform to model driving behaviors and dynamic interactions in mixed traffic flow. This is essential for understanding actual traffic dynamics, optimizing traffic management strategies, enhancing road safety, and advancing the development of ITS.