# Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
## 2 Related Literature  
Multiple studies have investigated mixed traffic situations utilizing traffic flow simulation in the past. Most of them considered different assumptions regarding AV driving behavior by applying various simulation software tools and models for car-following (CF) and lane-changing (LC). A majority focused on analyzing the effects of varying AV penetration rates and AV functionalities (low-level, highly automated vehicles). Additionally, a variety of criteria were used to analyze the results in different road network segments. In the course of our literature research, we tried to identify these differences among relevant previous studies, which are listed in Table 1. Some of this research work has also been described in review papers [5, 7, 8, 9], which focused on TFS-based analyses of automated driving. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Simulation Platform and** & **Network** & **AV** & **AV** & **Evaluation** & **Study** \\
**Applied AV Models** & & **Functionalities** & **Rates** & **Criteria** & **Study** \\ \hline PTV Vissim & Motorway & cautious/normal/ & 0–100\% & max. traffic volume, & \\ CF: Wiedemann99 [10] & On-/Off-Ramp & all-knowing AVs & (20\% steps) & average delay & [11] \\ LC: not mentioned & & & & & \\ \hline not mentioned & & & & & \\ CF: IDM+ [12] & Motorway & low-level AVs & 0–100\% & travel time, & \\ LC: LMRS [13] & On-Ramp & ACC & (10\% steps) & max. traffic volume & \\ \hline SUMO & Road & AVs of SAE & 0\%, 100\% & & \\ CF: IDM [15] & networks & L2, L3, L4 & and 2 mixed & average speed, delay & \\ LC: LC2013 [16] & & & & & \\ \hline Aimsun & Motorway & CAVs of SAE & 0–100\% & travel flow dynamics, & \\ CF: Gipps [18] & On-Ramp & L1, L2, L3, L4 & (9 scenarios) & number of & \\ LC: Gipps [18] & & & & & traffic conflicts \\ \hline Aimsun & & & & & \\ CF: IDM [15] and & Motorway & ACC & 0–100\% & max. traffic volume, & \\ ACC-model [20] integrated & On-Ramp & & (25\% steps) & delay differences, & \\ via MicroSDK tool & & & & speed differences & \\ LC: not mentioned & & & & & \\ \hline PTV Vissim & Motorway & & 0\%, 10\%, & & \\ CF: Wiedemann99 [10] & hard-shoulder & & 30\%, 50\%, & kilometers traveled & \\ LC: Vissim default model & opening & & 70\%, 90\%, & & per time unit & \\  & scenario & & 100\% & & \\ \hline \end{tabular}
\end{table} 
Table 1: Literature review: AV simulation studies. Since the behavior of automated and connected driving is predictable and deterministic, unlike the stochastic behavior of human drivers, a common approach is to parameterize the standard models provided by simulators in order to reflect the behavior of automated vehicles [7]. In [PERSON] et al. [11], pre-defined driving parameter sets for the Wiedemann99 car-following model [10; 30] and the Vissims internal lane-change model [30] were applied. A simulation experiment was conducted to analyze the impact on travel time and vehicle throughput. The results showed a capacity increase as soon as the capabilities of automated vehicles allowed them to maintain smaller headways. The impact on future traffic flow due to the gradual introduction of automation technology was analyzed by [PERSON] et al. [14]. Within their research they performed a simulation-based experiment on a uniform 19-kilometer motorway corridor, including an on-ramp which acted as a bottleneck. They adapted the Intelligent Driver Model (IDM) [12] for car-following and applied the LMRS (Lane-change Model with Relaxation and Synchronization) for lane-change modeling [13]. Despite major uncertainties, their results allowed them to assume that traffic capacity will slightly decrease in the early transition phase with low-level automated vehicles. [PERSON] et al. [17] investigated the impact of automated vehicles on average speed and delay in three different road networks in the US utilizing SUMO. For car-following the IDM was used, which was proposed by [PERSON] et al. [31] for modeling ACC-equipped