A Framework for Evaluating Energy and Emissions of Connected and Automated Vehicles through Traffic Microsimulations [Paper]

Eilbert, Andrew; Jackson, Lauren; Noel, George; Smith, Scott · 2017 · ROSA P / John A. Volpe National Transportation Systems Center (U.S.)

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Summary

This paper addresses the environmental impacts of Connected and Automated Vehicles (CAVs), specifically focusing on energy efficiency and air quality benefits derived from smoother driving behaviors. While much CAV research prioritizes safety and network performance, this study investigates whether near-term technologies, such as Cooperative Adaptive Cruise Control (CACC), can reduce fuel consumption and tailpipe emissions. The authors propose a three-layered modeling framework that integrates a CAV driving behavior model, a microscopic traffic simulation, and a fleet-based modal emissions model to assess these impacts. The methodology employs PTV Vissim traffic microsimulation software to generate high-resolution (10 Hz) vehicle trajectory data for a real-world network: Interstate 91 northbound near Springfield, Massachusetts. The study compares three scenarios: a baseline using the default Wiedemann 99 car-following model, a CACC scenario using the MIXIC driving behavior model, and a modified baseline where Wiedemann oscillation parameters are set to zero. Traffic volumes and speed distributions were calibrated using real-world data from the Massachusetts Department of Transportation. The resulting trajectories were processed into operating modes based on vehicle-specific power, speed, and acceleration, and then evaluated using the EPA’s Motor Vehicle Emission Simulator (MOVES2014a) to quantify hourly emissions and energy consumption. The results indicate that CACC driving produces notable reductions in fine particulate matter (PM2.5) and carbon monoxide (CO) compared to the baseline, with mean reductions of 25.24% and 20.12%, respectively. CACC also yielded slight benefits for nitrogen oxides (NOx) and volatile organic compounds (VOC). However, CACC did not improve fuel economy, showing a negligible -0.23% change in energy consumption. In contrast, the scenario with Wiedemann oscillations set to zero demonstrated little to no benefit, with negligible changes in emissions and energy use. Microsimulation performance metrics showed that CACC narrowed the range of vehicle speeds and smoothed accelerations, particularly in congested links, though it did not significantly increase average speeds. The significance of this work lies in the validation of a streamlined framework for evaluating CAV environmental impacts using high-resolution trajectory data. The findings suggest that while CACC can improve air quality by reducing specific pollutants, it does not necessarily enhance fuel efficiency in this context. The authors conclude that using independent driving behavior models is preferable to modifying default simulation parameters for assessing CAV technologies. They recommend that future emissions modeling tools, such as MOVES, incorporate features to handle custom operating mode distributions to facilitate larger-scale analysis of CAV adoption.

Key finding

CACC driving produced notable reductions in fine particulate matter (PM2.5) and carbon monoxide (CO) over the baseline but did not have an effect on fuel economy.

Methodology

simulator

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• v2x connected vehicle

Information type

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• Methodological Resource: tool software
• Theoretical Contribution: computational model