# Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
## 4 Experiment and Results
### Simulation Results
These low time headways cannot be observed when analyzing the gap acceptance behavior of the developed automated driving functions, as a safety distance to surrounding vehicles was considered within the AV lane change model. However, a clear shift towards smaller accepted time gaps was visible when AVs were allowed to execute non-compliant trajectories, which is the case when comparing the results from scenario C3 and D3 (see Figure 12b,c). To analyze the impact on traffic flow, spatiotemporal speed profiles are presented for varying penetration rates and different AV driving regulations (Figures 13-15). The distance before and after the on-ramp area is shown on the x-axis. The color indicates the observed average speed in space and time with a resolution of 25 m and 30 s, derived from the simulated trajectory data. The red colored areas indicate congested traffic. It can be seen that free-flow traffic was faster on the main road compared with the on-ramp link. As described in [PERSON] and [PERSON] [43], the speed on the on-ramp link usually decreases slightly before the on-ramp area, due to increased road curvature. Therefore, the desired speed was slightly lowered 200 m before the on-ramp area, which is indicated in orange. Since the traffic volume decreased after two thirds of the simulation time, the dissolution of congestion can also be observed. For the base case scenario (see Figure 13), congested traffic lasted for 43.0 and 23.5 min on the main road link and the on-ramp link, respectively. Figure 14 presents the spatiotemporal speed profiles of scenario A1 and C1. In both cases, non-compliant trajectories were prohibited and the desired time headway \(\tau_{min}\) was set to 0.9s, which corresponds to rather risky behavior. While the AV penetration rate was set to 25% in scenario A1, scenario C1 considered an AV penetration rate of 50%. The results show that congestion was reduced on the main road if the AV penetration rates were increased. It needs to be mentioned that this was not the case if the desired time headway was set to a higher level, as shown by the results in Table 6. Moreover, congestion was not reduced at the on-ramp link when comparing the results with the base case scenario A0 in Figure 13. Increasing the AV penetration rate from 25% to 50% increased the maximum congestion length from 0.75 km to 0.83 km. In scenario C1, congestion on the on-ramp area started to emerge much later than in scenario A1. This led to a shorter time period of congested traffic on the on-ramp link. Next, we focus on the spatiotemporal speed profiles for scenarios C3 and D3, shown in Figure 15. Both scenarios were simulated with an AV penetration rate of 50% and the desired time headway was set to 1.8 s. In scenario C3, non-compliant mode was prohibited, while it was allowed in scenario D3. First, when comparing Figures 14b and 15a, it can be seen that congestion increased drastically if the AVs' desired time headway was increased from 0.9 s to 1.8 s. Additionally, the comparison of scenarios C3 and D3 shows that only minor differences could be observed on the main road. In both scenarios, the network was fully congested. In scenario C3, this was the case for a duration of 21.5 min. Allowing AVs to execute non-compliant trajectories prolonged this state by nine minutes. However, the overall duration of congestion in the on-ramp area did not change substantially. While the on-ramp area of the main road was congested for 85.5 min in scenario C3, this value increased slightly to 86.0 min in scenario D3. At the same time, major differences can be observed at the on-ramp link as it was possible to reduce congestion significantly when AVs were allowed to execute non-compliant trajectories. On the on-ramp link, the maximum congestion length was 1.85 km in scenario C3. This was reduced by 40% to 1.15 km in scenario D3. Figure 13: Spatiotemporal speed profile of scenario A0 (Base case).