Most people usually drive their familiar routes to work and are concerned about the traffic on their way to work. If a driver's preferred route is known, the traffic congestion information on his/her way to work will be reported in time. However, the current navigation systems focus on planning the shortest path or the fastest path from a given start point to a given destination point. In this paper, we present a novel personalized route planning framework that considers user movement behaviors. The proposed framework comprises two components, familiar road network construction and route planning. In the first component, we mine familiar road segments from a driver's historical trajectory dataset, and construct a familiar road network. For the second component, we propose an efficient route planning algorithm to generate the top-k familiar routes given a start point and a destination point. We evaluate the performance of our algorithm using a real dataset, and compare our algorithm with an existing approach in terms of effectiveness and efficiency.