Moving Object Linking Based on Historical Trace

Fengmei Jin, Wen Hua, Jiajie Xu, Xiaofang Zhou

Abstract: The prevalent adoption of GPS-enabled devices has witnessed an explosion of various location-based services which produce a huge amount of trajectories monitoring an individual's movement. This triggers an interesting question: is movement history sufficiently representative and distinctive to identify an individual? In this work, we study the problem of moving object linking based on their historical traces. However, it is non-trivial to extract effective patterns from moving history and meanwhile conduct object linking efficiently. To this end, we propose four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) to construct the personalised signature from the historical trace. Moreover, we formalise the problem of moving object linking as a k-nearest neighbour (k-NN) search on the collection of signatures, and aim to improve efficiency considering the high dimensionality of signatures and the large cardinality of the candidate object set. A simple but effective dimension reduction strategy is introduced in this work, which empirically outperforms existing algorithms including PCA and LSH. We propose a novel indexing structure, Weighted R-tree (WR-tree), and two pruning methods to further speed up k-NN search by combining weight and spatial information contained in the signature. Our extensive experimental results on a real world dataset verify the superiority of our proposals, in terms of both accuracy and efficiency, over state-of-the-art approaches.