With the increasing proliferation of position technologies, there comes huge volumes of trajectory data, which are used in many modern applications such as path planning and location based services. Accurate road network matching can improve the service quality of these new applications. However, the low sampling trajectories bring a major challenge for map-matching. This paper studies the problem of matching individual law-sampling trajectory to a dynamic multi-criteria road network based on user's driving preferences. First, a driving preference model in the dynamic road traffic network is proposed. Based on this model, a two-stage map-matching algorithm is developed. While local matching searches multiple local likely Skyline paths, a global matching dynamic programming algorithm is designed and the most probable k global paths are selected as the matching result. Experiments show that the proposed method is effective and efficient.