# Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways
## 5 Numerical Simulation  
### Experimental Design  
To evaluate traffic efficiency, dynamics, safety, and environment in mixed traffic flow consisting of CAVs, AVs, and HDVs for both cars and trucks, all experiments were carried out on the circular road and highway. The simulation road networks consist of a 1 km three-lane ring road and highway with an on-ramp, as illustrated in Figures 3 and 4. Based on the driving behaviors of different vehicle types, these types can be categorized as connected and autonomous cars, connected and autonomous trucks, autonomous cars, autonomous trucks, human-driven cars, and human-driven trucks. In these experiments, these vehicles were represented in red, orange, blue, yellow, green, and gray, respectively. The network configuration is detailed in Table 1. The proportion of cars to trucks on the roads is usually affected by various factors such as location, traffic flow, time of day, and the use of a specific road segment. In general, passenger vehicles constitute the majority of vehicles on the roads, with trucks accounting for a smaller share. In the simulation experiment with a total traffic load of 2500 vehicles, the ratio of cars to trucks is 7:3. The traffic load is randomly generated based on the Python script, and each simulation step lasts 0.1 s. Considering the penetration rates of various vehicle types, all experiments are carried out using a simulation platform that integrates Python and SUMO to evaluate the traffic performance of heterogeneous traffic. To account for the randomness of mixed traffic flow, each experimental scenario was conducted 10 times, and the average of these runs was taken as the result. In a traffic condition where HDVs, AVs, and CAVs coexist, CF models based on IDM, ACC, and CACC were utilized to simulate and analyze the driving behavior of the mixed traffic. These models are employed to describe the dynamic interactions and responses of various vehicle types in complex traffic situations, with their parameters derived from the literature [51; 52] as in Table 2. The vehicle types for cars and trucks are labeled as \begin{table}
\begin{tabular}{c c c} \hline \hline
**Road Network** & **Ring road** & **Highway** \\ \hline \multirow{4}{*}{Road section length} & Top: 0.5 km & \\  & Right: 0.5 km & Highway: 1 km \\  & Bottom: 0.5 km & On-ramp: 0.2 km \\  & Left: 0.5 km & \\ \hline \multirow{4}{*}{Allowed road speed} & Lane 0: 80 km/h & Lane 0: 80 km/h \\  & Lane 1: 100 km/h & Lane 1: 100 km/h \\  & Lane 2: 120 km/h & Lane 2: 120 km/h \\  & & On-ramp: 60 km/h \\ \hline \multirow{4}{*}{Allowed road vehicles} & Lane 0: cars, trucks & Lane 0: cars, trucks \\  & Lane 1: cars, trucks & Lane 1: cars, trucks \\  & Lane 2: cars & Lane 2: cars \\  & & On-ramp: cars, trucks \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The network set-up for the ring road and freeway. Figure 4: The schematic diagram of the highway. Figure 3: The schematic diagram of the ring road. \"passenger\" and \"truck,\" respectively. For manually driven vehicles, taking into account the potential imperfections in real-world driving, such as fatigue, distraction, or slow reactions, driving errors are introduced through a driver state device within the car-following model and lane-changing model to more accurately simulate driver behavior. In these experiments, the settings for all parameters were taken from the literature [53] as in Table 3.  
The proportion of vehicles with different levels of automation is a variable that evolves with technological progress and time and is closely related to policies, infrastructure, technological maturity, and public acceptance in different countries and regions. According to the previous literature [54], the parameter values for the ACC and CACC car-following models for cars and trucks in the experiment are presented in Table 4.  
Table 5 shows different proportions of AVs, CAVs, and HDVs in mixed traffic scenarios on the road networks.