Popular route planning systems (Windows Live Local, Yahoo! Maps, Google Maps, etc.) generate driving directions using a static library of roads and road attributes.\u00a0 They ignore both the time at which a route is to be traveled and, more generally, the preferences of the drivers they serve.\u00a0 We present a set of methods for including driver preferences and time-variant traffic condition estimates in route planning.\u00a0 These methods have been incorporated into a working prototype named TRIP.\u00a0 Using a large database of GPS traces logged by drivers, TRIP learns time-variant traffic speeds for every road in a widespread metropolitan area.\u00a0 It also leverages a driver\u2019s past GPS logs when responding to future route queries to produce routes that are more suited to the driver\u2019s individual driving preferences.\u00a0 Using experiments with real driving data, we demonstrate that the routes produced by TRIP are measurably closer to those actually chosen by drivers than are the routes produced by routers that use static heuristics.<\/p>\n","protected":false},"excerpt":{"rendered":"
Popular route planning systems (Windows Live Local, Yahoo! Maps, Google Maps, etc.) generate driving directions using a static library of roads and road attributes.\u00a0 They ignore both the time at which a route is to be traveled and, more generally, the preferences of the drivers they serve.\u00a0 We present a set of methods for including 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