The popularity of location-acquisition devices has led to a rapid increase in the amount of trajectory data collected. The large volume of trajectory data causes the difficulties of storing and processing the data. Various trajectory compression methods are therefore proposed to deal with these problems. In this paper, we overview the existing road-network-constrained trajectory compression methods and propose a novel classification based on the features leveraged by them. We also propose new methods that fill in the research blanks indicated by the classification. We conduct a thorough comparison among the existing and new road-network-constrained trajectory compression methods. The performances of the methods are studied via various metrics on real-world dataset. We make new discoveries regarding the performances and the scalability of existing methods, and provide guidelines of road-network-constrained trajectory compression for various scenarios. © 2016 IEEE.