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

TL;DR

This paper introduces CiNCT, a novel compressed data structure for vehicular trajectories that enables efficient pattern matching and decompression, leveraging relative movement labeling and network sparsity for superior compression and query performance.

Contribution

The paper proposes a new trajectory compression method based on FM-index with relative movement labeling, improving compression and query efficiency over existing approaches.

Findings

Significant data size reduction compared to existing methods

Faster query processing due to PseudoRank and network sparsity exploitation

Theoretical guarantees of high compressibility and pattern matching capabilities

Abstract

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