Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
NEEMAT is a web-based decision-support tool that predicts vehicle and power-plant emissions plus fuel/energy consumption under rising EV adoption for Atlanta, Los Angeles, New York, and Seattle. A feedforward neural network trained on MOVES estimates tract-level vehicle energy use and CO2/NOx/PM2.5 by speed, vehicle type, fuel, and age, while a macroscopic traffic model captures flow effects. Grid-side CO2/CH4/N2O from EV charging are forecast with a Meta-Prophet model trained on Cambium. Users can explore 24-hour profiles and five-year outlooks, compare scenarios, and export results. Findings show that despite substantial EV uptake, mixed fleets and grid responses can raise total emissions, underscoring the need for integrated transportation–power planning.
Included in
GRM-1153 National Energy and Emission Modeling and Analysis Tool
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
NEEMAT is a web-based decision-support tool that predicts vehicle and power-plant emissions plus fuel/energy consumption under rising EV adoption for Atlanta, Los Angeles, New York, and Seattle. A feedforward neural network trained on MOVES estimates tract-level vehicle energy use and CO2/NOx/PM2.5 by speed, vehicle type, fuel, and age, while a macroscopic traffic model captures flow effects. Grid-side CO2/CH4/N2O from EV charging are forecast with a Meta-Prophet model trained on Cambium. Users can explore 24-hour profiles and five-year outlooks, compare scenarios, and export results. Findings show that despite substantial EV uptake, mixed fleets and grid responses can raise total emissions, underscoring the need for integrated transportation–power planning.