This research assessed the changes in Metropolitan Atlanta Rapid Transit Authority (MARTA) service configurations by reviewing the pre-pandemic vs. during-pandemic General Transit Feed Specification (GTFS) files.
This data is from the project, Dynamic Routing for Ridesharing. The data is randomly generated within grids where each unit of the grid represents a 1-mile by 1-mile square. It is used to generate the origins and destinations of passengers.
Researchers examined the possibility of ride-sharing being able to mitigate traffic congestion by developing a two-stage algorithm to solve the routing problem with ride-share in real-time within a context where ride-sharing drivers are traveling toward their own destinations while making detours to serve passengers with flexible pickup and drop-off locations.
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.
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.