Abstract: Map-Matching is one of the important parts of the vehicular navigation system. This paper provided a brief introduction of various map matching algorithm firstly, a novel integrated algorithm combining route prediction with uncertain reasoning was proposed based on previous researching. In the proposed algorithm, the cloud model, a powerful tool to perform uncertain converting between numerical quantitative analysis to conceptual qualitative analysis, was firstly used to calculate the credibility of positioning point to candidate roads by uncertain reference with current locating data. Then, a Hidden Markov Model (HMM) was built to predict the driver’s routes and destinations. Through redesigning observation function in HMM, an integrated map matching algorithm was established by combining route predicting information with current matching approach. Additionally, the learning algorithm was carried out to support the algorithm and update the information. Finally, the experimental results demonstrate the effectiveness of the proposed algorithm: it can predict a driver’s routs to improve the accuracy and real-time characteristic of map matching algorithm according to the utility of pre-matching.