Abstract
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 chapter, we first propose an online trajectory data compression algorithm which works on the basis of the SD-Matching algorithm. Similar to the SD-Matching algorithm, the newly online data compression makes use of the heading change at intersections, namely Heading Change Compression (HCC), to find concise and compact trajectory representation. Furthermore, we also implement both SD-Matching and HCC algorithms in a real system called VTracer running on the Android platform. Since both online map-matching and compression are resource-hungry, and GPS devices cannot afford the heavy computation tasks, we offload such tasks to the nearby smartphones of drivers by leveraging the idea of mobile edge computing. We conduct experiments to evaluate the effectiveness and efficiency of the proposed HCC algorithm using real-world datasets in the city of Beijing, China. We deploy the system in the real world in the city of Chongqing, China. Experimental results in real cases demonstrate the excellent performance of HCC algorithm and VTracer system.
Part of this chapter is based on two previous work:
C. Chen, Y. Ding, S. Zhang, Z. Wang and L. Feng, “TrajCompressor: An online map-matching-based trajectory compression framework leveraging vehicle heading direction and change,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 5, pp. 2012–2028, Apr. 2019.
C. Chen, Y. Ding, Z. Wang, J. Zhao, B. Guo and D. Zhang, “VTracer: When online vehicle trajectory compression meets mobile edge computing,” IEEE Systems Journal, vol. 14, no. 2, pp. 1635–1646, Aug. 2019.
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Chen, C., Zhang, D., Wang, Y., Huang, H. (2021). Trajectory Data Compression. In: Enabling Smart Urban Services with GPS Trajectory Data. Springer, Singapore. https://doi.org/10.1007/978-981-16-0178-1_2
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DOI: https://doi.org/10.1007/978-981-16-0178-1_2
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