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

Khan, Zawar Hussain; Ali, Faryal; Gulliver, Thomas Aaron; Alsaffar, Mohammad; Altamimi, Ahmed B. · 2026 · DOAJ

DOI: 10.1038/s41598-026-37851-x

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Summary

This paper addresses the challenge of characterizing traffic emissions from Connected Autonomous Vehicles (CAVs) by developing a microscopic traffic model that explicitly links traffic density to carbon dioxide (CO2) emissions. Motivated by the rapid growth of global traffic congestion and greenhouse gas emissions, the authors aim to improve upon existing models, such as the Intelligent Driver (ID) model, which rely on constant parameters and fail to account for CAV behavior or emission dynamics. The study seeks to create a more realistic representation of traffic flow that captures how vehicle interactions and density variations influence environmental impact. The methodology combines field experiments with theoretical modeling and simulation. Field data were collected during morning and evening trips on two routes in Peshawar, Pakistan, using an On-Board Diagnostic-II scanner to record real-time emission data from the vehicle’s engine control unit. Regression analysis of this data established a quadratic relationship between CO2 emissions and traffic density. The authors then modified the ID model by replacing its constant acceleration exponent with a dynamic variable derived from this emission-density relationship, incorporating CAV-specific parameters such as reduced reaction time and distance headway. The resulting model was discretized using the Euler technique and simulated in MATLAB on a 1000-meter circular road, allowing for a direct comparison with the standard ID model under identical boundary conditions. The results demonstrate that the proposed model yields more stable and realistic traffic behavior than the ID model. Stability analysis revealed that the new model produces negative eigenvalues for traffic oscillations, indicating string stability where disturbances dampen over time. In contrast, the ID model exhibited higher variability in speed, density, and acceleration. Statistical analysis confirmed that the proposed model had lower variability, reflecting smoother traffic flow. Specifically, the integration of CAV parameters allowed for dynamic adjustments in speed and headway based on surrounding traffic, leading to fewer acceleration and deceleration events. Consequently, the simulations showed that the proposed model resulted in lower CO2 emissions compared to the ID model, as it better captured the physical realities of dense traffic and autonomous vehicle responsiveness. The significance of this work lies in its contribution to the development of environmentally aware microscopic traffic models for future transportation networks. By coupling traffic dynamics with emission modeling, the study provides a tool for assessing the environmental benefits of CAV adoption. The findings suggest that CAVs, when modeled with realistic emission-dependent behaviors, can significantly mitigate congestion and reduce air pollution by promoting stable traffic flow. This approach offers a more accurate framework for evaluating the impact of autonomous vehicles on air quality and energy consumption, supporting efforts to design sustainable transportation systems.

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• traffic density