Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 21, Issue 5, Pages 2012-2028Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2910591
Keywords
Trajectory; Global Positioning System; Roads; Presses; Data centers; Urban areas; Task analysis; Vehicle trajectory compression; map-matching; heading direction; heading change; mobile environment
Categories
Funding
- National Key Research and Development Project of China [2017YFB1002000]
- National Science Foundation of China [61872050, 61602067, 71601024]
- Fundamental Research Funds for the Central Universities [2018cdqyjsj0024, 2019cdxyjsj0022]
- Frontier Interdisciplinary Research Funds for the Central Universities [106112017cdjqj188828]
- Chongqing Basic and Frontier Research Program [cstc2018jcyjAX0551]
- Ministry of Education in China Humanities and Social Sciences Youth Foundation [16yjc630169]
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Massive and redundant vehicle trajectory data are continuously sent to the data center via vehicle-mounted GPS devices, causing a number of sustainable issues, such as storage, communication, and computation. Online trajectory compression becomes a promising way to alleviate these issues. In this paper, we present an online trajectory compression framework running under the mobile environment. The framework consists of two phases, i.e., online trajectory mapping and trajectory compression. In the phase of online trajectory mapping, we develop a light-weighted yet efficient map matcher, namely, Spatial-Directional Matching (SD-Matching), to align the noisy and sparse GPS points upon the underlying road network, which fully explores the usage of vehicle heading direction collected from the GPS trajectory data. In the phase of online trajectory compression, we propose a novel compressor based on the heading change at intersections, namely, Heading Change Compression (HCC), aiming at finding a concise and compact trajectory representation. Finally, we conduct experiments to evaluate the effectiveness and efficiency of the proposed framework using real-world datasets in the city of Beijing, China. We further deploy the system in the real world in the city of Chongqing, China. The experimental results demonstrate that: 1) the SD-Matching algorithm achieves a higher mean accuracy but consumes less time than the state-of-the-art algorithm, namely, Spatial-Temporal Matching (ST-Matching) and 2) the HCC algorithm also outperforms baselines in trading-off compression ratio and computation time.
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