# Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways
## 5 Numerical Simulation
### Results and Discussion
#### 5.2.4 Environmental Impacts  
To investigate the environmental impact of mixed traffic flow on the ring road and highway, CO, HC, and NOx emissions were plotted. Specifically, in experimental scenarios involving different proportions of mixed traffic flow, the process began by generating emission output files (emission-output) based on the simulation configuration file (_._sumocfg). Subsequently, Python scripts were employed to parse and aggregate emission data from different experimental scenarios, producing a comprehensive analysis of emissions. Finally, the processed data were visualized using Excel, providing a clear and intuitive representation of the emission characteristics across various experimental settings.  
Figures 13 and 14 depict the reduction in traffic emissions in mixed traffic on the ring road and highway, respectively.  
The introduction of CAVs and AVs led to a reduction in CO, HC, and NOx emissions in all experimental scenarios. However, traffic emissions differed due to differences in the scenarios and the proportion of mixed traffic flow. In particular, Scenario 1 showed the least improvement in CO, HC, and NOx emissions compared to the other scenarios. This suggested that the low number of autonomous vehicles in Scenario 1 is insufficient to significantly enhance overall traffic flow efficiency, resulting in a smaller reduction in traffic emissions. Conversely, Scenario 3 exhibited the largest decrease in CO, HC, and NOx emissions compared to the other scenarios. With a lower proportion of HDVs, the introduction of CAVs and AVs can better control speed and spacing, reduce traffic fluctuations and instability, and thereby further lower emissions. The emission reduction in Scenario 2 fell between that of Scenario 1 and Scenario 3, suggesting that the moderate number of CAVs and AVs in Scenario 2 can improve traffic flow and reduce emissions to some extent. However, the impact is limited due to the lower proportion of CAVs and AVs.  
Figure 12: The average number of potential conflicts for different scenarios on the highway.  
With the growing number of CAVs and AVs, there has been a notable decrease in traffic emissions for different road networks. This suggests that autonomous driving technology substantially reduces fuel consumption and pollutant emissions by optimizing driving behaviors, such as reducing sudden braking and maintaining a steady speed.  
In summary, compared to the baseline scenario, the implementation of the JAD strategy for speed control in the mixed traffic flow effectively reduces traffic waves, enhances traffic efficiency, stability, and safety, while also reducing pollution emissions. This finding enhances the understanding of the dynamic characteristics of heterogeneous traffic flow in complex mixed traffic scenarios and their implications for traffic management. Furthermore, the results could provide valuable insights for policy-making on the integration of autonomous vehicles into current traffic systems and the planning of future road networks. However, this study has some limitations, as it does not consider factors such as adverse weather conditions, emergencies, or the long-term environmental and economic impacts of integrating autonomous vehicles.  
Figure 13: The reduction in traffic emissions in mixed flow on the ring road. (**a**) CO emissions; (**b**) HC emissions; (**c**) NOx emissions.