# Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
## 2 Related Literature
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Simulation Platform and** & **Network** & **AV** & **AV** & **Evaluation** & **Study** \\
**Applied AV Models** & & **Functionalities** & **Rates** & **Criteria** & **\\ \hline SUMO & Motorway and & ACC & 0–100\% & max. traffic volume, average speed, spatiotemporal & [23] \\ CF: ACC, CACC-model [20] & urban & CACC & (25\% steps) & spatiotemporal & \\ LC: not mentioned & ring road & & & speed-profiles & \\ \hline PTV Vissim & Motorway & CAV & 0\%, 10\%, & max. traffic volume, average speed, number of & [24] \\ LC: new CAV LC model & On-/Off-Ramp & & 70\%, 100\% & number of & \\ integrated via DLL-interface & & & & lane changes & \\ \hline PTV Vissim & & CAV & & throughput, average speed, number of & [25] \\ CF: new AV/ACC model & Motorway & HAV & 0–100\% & (20\% steps) & \\ LC: new AV LC model & On-/Off-Ramp & (highly & (20\% steps) & number of & \\ CF and LC integrated & & automated & & lane changes & \\ via DLL-interface & & vehicles) & & & \\ \hline SUMO & Motorway & AV & Multiple & travel time, & \\ CF: Sumo ACC/C-ACC & on-ramp and & CAV & scenarios & number of & \\ LC: SUMO LC 2013 [16] & ring-road & & & 0–90\% & lane changes & \\ \hline PTV Vissim & Motorway & AV with & 10–90\% & average speed, number of & [27] \\ CF: Wiedemann99 [10] & lane closure & 9 aggressiveness & (10\% steps) & & detected crashes & \\ LC: Vissim default model & scenario & levels & & & \\ \hline PTV Vissim & Motorway & CAV-enabled & 0\% CAVs and & traffic volume, & \\ CF: new ACC/Platooning & & lane closure & lane change & 3 signalization & average speed & \\ model; LC: new lane change & scenario & signalization & scenarios with & \\ strategy and Vissim default & & strategy & 100\% CAVs & & \\ model; CF and LC strategy & & & & \\ integrated via DLL-interface & & & & & \\ \hline PTV Vissim & Motorway & CAVs & 0\%, 30\%, & traffic volume, & \\ CF: Wiedemann99 & lane closure & reacting on & 50\%, 70\% & spatiotemporal & \\ LC: Vissim default model & scenario & speed control & strategy & & speed-profiles & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._vehicles. They found that average speeds were higher with increasing penetration rates, especially in a fully automated environment. The effects on traffic safety in a mixed traffic environment were investigated in [PERSON] et al. [19]. To model different levels of automated driving, [PERSON] car-following and lane-change models [18] were adapted. Their results showed that traffic was harmonized, since speed differences and acceleration indicators were decreased. To evaluate car-following behaviour, [PERSON] et al. [21] analyzed and compared the IDM and an ACC model proposed by [PERSON] and [PERSON] [20]. The models were integrated in Aimsun using the MicroSDK tool, which enables users to integrate external behavioral models. They found that the IDM did not respond to speed changes quickly enough, which led to headway errors. Moreover, a time headway of 2 s caused a degradation in traffic performance, while shorter time headways increased traffic efficiency. In the research by [PERSON] et al. [22], VISSIM's internal models were parametrized differently to model human-driven and connected automated vehicles in order to test the effects of mixed traffic on hard-shoulder running. Their simulations showed a pronounced positive influence of hard-shoulder running on both traffic efficiency and safety as penetration rates increased. Rather than using standard models with different parameter configurations for human-driven vehicles and automated driving functions, another approach is to develop models from scratch. A car-following model for ACC and CACC was integrated into the traffic flow simulation tool SUMO by [PERSON] et al. [23]. To analyze the effects on traffic flow, simulations with different penetration rates were executed, and it was shown that the implemented systems have the potential to increase capacity, even at low penetration rates, as time headways were reduced for AVs. The studies by [PERSON] et al.