A microscopic traffic characterization considering the impact of density on carbon emissions from CAVs

The continuous growth of traffic and transportation activities across the globe has led to persistent congestion and greenhouse gas emissions from vehicles. Thus, it is important to develop a traffic model to mitigate congestion and air pollution. In this paper, a microscopic traffic model characterizing traffic emissions based on density is proposed. Field experiments were performed, and the data collected were analyzed to obtain the relationship between density and $$\:\text{C}{\text{O}}_{2}$$ emissions. Then, the connected autonomous vehicle (CAV) parameter was incorporated, and a new traffic model was developed by integrating it into the intelligent driver (ID) model. The ID model characterizes traffic based on a constant exponent and ignores traffic emissions and CAV behavior. The proposed model can provide details of vehicle emissions under varying traffic densities. Further, the stability analysis illustrates that the proposed model results in more stable traffic compared to the ID model, as it is based on real-world traffic parameters. For performance analysis, the proposed and ID models are compared over a $$\:1000$$ m circular road using MATLAB. Results suggest that the proposed model traffic behavior is more realistic and the variations in traffic speed, density, and acceleration based on the traffic emissions are small compared to the ID model, thereby resulting in lower emissions. Further, the statistical analysis indicates a lower variability in the proposed model, reflecting lower emissions and stable traffic behavior.