Compression methods for trajectory data under road network constraints

Abstract

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 thesis, we study the problem of trajectory compression under road network constraints. We summarize the existing road-network-constrained trajectory compression methods and propose a classification based on the features leveraged by them. We 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. We also design a new road-network-constrained trajectory compression framework composed of several coordinating methods which has an excellent performance in both spatial and temporal compression. The framework is also able to support location-based services by being able to answer several basic spatio-temporal queries. We conduct extensive experiments to verify the efficiency and effectiveness of our proposed framework using real-world trajectory data. The results strongly advocate the performance of the proposed framework.

Date of Award	2017
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology