Deducing individual driving preferences for user-aware navigation

We study the problem of learning individual route preferences of drivers. Most current route planning services only compute shortest or quickest paths. But many other criteria might play a role for a user to prefer a certain route, as, e.g., fuel consumption, jam likeliness, road conditions, scenicness of the route, turns, allowed maximum speeds, toll costs and many more. Specifying the importance of each criterion manually is a non-trivial, unintuitive and time consuming undertaking for a user. Therefore, we develop approaches that deduce such preferences automatically based on paths previously driven by the user. We present an LP-formulation of the problem making use of a Dijkstra-based separation oracle. The resulting algorithm runs in polynomial time and allows for the user preference computation in few seconds even if several hundred routes are taken into account. Our experiments show that new route suggestions based on these learned preferences reflect the users definition of an optimal route very well.