Dynamically computing fastest paths for intelligent transportation systems

In this paper I present a new approach to gathering data for intelligent transportation system applications over a continuous-flow of traffic rather than at discrete locations, as is the case with many existing technologies. Loop detectors and video cameras, among other devices, currently provide the primary means for gathering data, though it has now become possible using mobile and GPS technology to gather the speed and location of each vehicle in real-time over a continuous flow, which will allow more novel applications, such as incident identification and hazard alerts, to be developed. In addition, as vehicles transmit updated speeds to the system, the fastest path of each commuter from his current location to his desired destination can be determined. The pre-computed class of algorithms determines fastest paths more efficiently than existing algorithms, with the assumption that the graph edges are rather static though the weights can change frequently. Different shortest and fastest path algorithms are presented and analyzed using FreeSim (http://www.freewaysimulator.com), which contains an implementation of all of the algorithms discussed.