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In this paper, we present a compressed data structure for moving objecttrajectories in a road network, which are represented as sequences of roadedges. Unlike existing compression methods for trajectories in a network, ourmethod supports pattern matching and decompression from an arbitrary positionwhile retaining a high compressibility with theoretical guarantees.Specifically, our method is based on FM-index, a fast and compact datastructure for pattern matching. To enhance the compression, we incorporate thesparsity of road networks into the data structure. In particular, we presentthe novel concepts of relative movement labeling and PseudoRank, eachcontributing to significant reductions in data size and query processing time.Our theoretical analysis and experimental studies reveal the advantages of ourproposed method as compared to existing trajectory compression methods andFM-index variants.

CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling