This study identifies heavily-trafficked freight truck routes of optimal distances in Georgia and quantifies electrification benefits for fleets operating on these corridors by integrating outputs from MOVES Matrix, the Argonne National Laboratory’s GREET Model, the Georgia Tech Fuel and Emissions Calculator (FEC), and other models into a web-based tool.
In this research, widely used car-following models are re-assessed for use in emission/fuel consumption estimation, with their impact on vehicle specific power and instantaneous engine work as comparative indices.
The research team will develop Python code to integrate TransitSim shortest transit path predictions for every origin-destination pair and departure time into regional activity-based travel demand model (ABM) outputs.
This research proposal will compare alkali activated materials, asphalt and “green” asphalt pavement technology through life cycle assessment and life cycle cost analysis, using the state of Georgia as a case study.
This study will develop MOVES-Matrix 3.0, a high-performance emission modeling system that uses emission rates pre-generated by iterating across all combinations of model input variables for 146,853 MOVES 3 model runs per region.
This project will further build on previous MOVES-Matrix projects by developing MOVES-Matrix 4.0. A case study of the emission and energy use rate outputs will verify that the matrix produces the same results as using MOVES directly.
In this proposed project, the team will finish the development of MOVES-Matrix 3.0 for the Atlanta Metropolitan Area within a new optimized modeling framework.
The proposed research examines the financial state of the practice for sidewalk asset management in the United States, taking economic, social and legal costs into consideration.
This research project links Georgia Tech’s newest shortest path calculator, BikewaySim, with the finished shortest path calculator for public transportation, TransitSim.
In the research, smart charging will be explored in the larger context of electric vehicle fleets carrying freight and/or people to assess its potential to again decrease charging costs, increase carbon-free energy usage, decrease spatially-concentrated peak demands on the grid, and lower infrastructure investment.
