Map matching is a key preprocessing step in the location-based service to match GPS points into a digital road network. Data analysis on the map matched trajectory data can be used to facilitate many real city computing applications such as intelligent transportation system and trip planning. This survey provides a systematic summary of existing research achievements of map matching. With the rapid development of urban traffic, the cost of acquiring and processing vehicle location information is increasing, low-sampling-rate GPS tracking data is growing, and the accuracy of existing algorithms is not adequate. In recent years, map matching algorithm based on hidden Markov model (HMM) has been widely studies. HMM can smoothly assimilate noisy data with path constraints by choosing a maximum likelihood path. The accuracy of HMM-based algorithms can reach 90% under certain conditions, which confirms the validity of map matching algorithm based on HMM at low sampling rate. A perspective of future work in this research area is also discussed.