Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
The Transportation Energy and Emission Modeling and Analysis Tool (TEEMAT) is a web-based decision-support framework for evaluating the environmental impacts of EV adoption across U.S cities. TEEMAT integrates a feedforward neural network trained on MOVES 4.0 for tract-level vehicle emissions (CO₂, NOₓ, PM₂.₅), a macroscopic traffic and activity-based model capturing congestion-driven emission spikes, and a Meta-Prophet model trained on NREL Cambium data for grid emissions (CO₂, CH₄, N₂O). Results show that while EV adoption reduces tailpipe emissions, rising travel demand and congestion-induced low speeds can significantly offset these gains underscoring that meaningful decarbonization requires coordinated transportation and energy grid strategies.
Included in
GRM-159-214 Transportation Energy and Emission Modeling and Analysis Tool (TEEMAT)
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
The Transportation Energy and Emission Modeling and Analysis Tool (TEEMAT) is a web-based decision-support framework for evaluating the environmental impacts of EV adoption across U.S cities. TEEMAT integrates a feedforward neural network trained on MOVES 4.0 for tract-level vehicle emissions (CO₂, NOₓ, PM₂.₅), a macroscopic traffic and activity-based model capturing congestion-driven emission spikes, and a Meta-Prophet model trained on NREL Cambium data for grid emissions (CO₂, CH₄, N₂O). Results show that while EV adoption reduces tailpipe emissions, rising travel demand and congestion-induced low speeds can significantly offset these gains underscoring that meaningful decarbonization requires coordinated transportation and energy grid strategies.