Analysis of Fastest and Shortest Paths in an Urban City Using Live Vehicle Data from a Vehicle-to-Infrastructure Architecture

In this paper, I perform an analysis of the time to traverse different paths from a source to a destination in an urban city based on data gathered by a vehicle-to-infrastructure (V2I) intelligent transportation system (ITS) architecture. Vehicle tracking devices have been installed in 15 vehicles that frequent the University of Alaska, Anchorage on a daily basis. This data has been analyzed using FreeSim (http://www.freewaysimulator. com) to determine the fastest path from the university to a destination in downtown Anchorage, which is on the other side of the city. During the period of this research, two vehicles departed from the university to the destination along the fastest path and shortest path, respectively, each day of the week at 5:00p.m. to determine the actual amount of time to traverse each path. The fastest path traversed proved to be faster than the shortest path 67% of the time, and the calculated fastest path proved to be faster than the shortest path 84% of the time. I provide an analysis of these paths and give reasons as to why the fastest path was not always faster than the shortest path.