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

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    Connected and automated vehicles (CAV) may deliver energy efficiency and air quality benefits by reducing traffic congestion and facilitating smoother driving behavior. This paper proposes a three-layered modeling framework for assessing the energy and emission impacts of first generation CAV technologies, such as cooperative adaptive cruise control (CACC). The framework tightly integrates 1) a CAV driving behavior model with 2) a microscopic traffic simulation model to create vehicle trajectory data and then evaluates those trajectories in 3) a fleet-based modal emissions model. In a case study to test this framework, we utilized the microscopic model for simulation of intelligent cruise control (MIXIC) to represent vehicles driving with CACC systems in PTV Vissim, traffic microsimulation software, on Interstate 91 (I-91) northbound near Springfield, Massachusetts with real-world traffic speed and volume data. High-resolution (10 hertz), simulated passenger car trajectories were processed into operating modes according to vehicle specific power, speed, and acceleration and then run through the Motor Vehicle Emission Simulator (MOVES) to quantify the hourly emissions and energy consumption on the I-91 network. We compared the results of baseline driving using the default Wiedemann 99 car-following model in Vissim against a scenario where all vehicles are CACC-enabled and another scenario where the Wiedemann oscillation parameters were set to zero. Our findings suggest that CACC driving will produce notable reductions in fine particulate matter (PM2.5) and carbon monoxide (CO) over the baseline but will not have an effect on fuel economy. The Wiedemann scenario without oscillations showed little to no benefit.
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