DAVT: An Error-Bounded Vehicle Trajectory Data Representation and Compression Framework
DAVT: An Error-Bounded Vehicle Trajectory Data Representation and Compression Framework
复制标题
DAVT：一种有误差的车辆轨迹数据表示和压缩框架
DOI：
10.1109/tvt.2020.3015214
复制
发表时间：
2020-08
影响因子：
6.8
通讯作者：
Yasha Wang
中科院分区：
文献类型：
--
作者：
Chao Chen;Yan Ding;Suiming Guo;Yasha Wang
An increasing number of vehicles are now equipped with GPS devices to facilitate fleet management and send their GPS locations continuously, generating a huge volume of trajectory data. Sending and storing such vehicle trajectory data cause sustainable communication and storage overheads. Trajectory data compression becomes a promising way to alleviate overhead issues. However, previous solutions are commonly carried out at the side of the data center after data having been received, thus saving the storage cost only. Here, we bring the idea of mobile edge computing and transfer the computation-intensive data compression task to the mobile devices of drivers. As a result, the trajectory data is reduced at the side of data generators before being sent out; thus, it can lower data communication and storage costs simultaneously. We propose <monospace>DAVT</monospace>, an error-bounded trajectory data representation, and a compression framework. Specifically, the trajectory data is reformatted into three parts (i.e., <monospace>D</monospace>istance, <monospace>A</monospace>cceleration & <monospace>V</monospace>elocity, and <monospace>T</monospace>ime), and three compressors are wisely devised to compress each part. For <monospace>D</monospace> and <monospace>AV</monospace> parts, a similar <italic>Huffman tree-forest</italic> structure is exploited to encode data elements effectively, but with quite different rationales. For the <monospace>T</monospace> part, the large absolute timestamps are transformed to small time intervals firstly, and different encoding techniques are adopted based on the data quality. We evaluate our proposed system using a large-scale taxi trajectory dataset collected from the city of Beijing, China. Our results show that our compressors outperform other baselines.
登录
查看更多内容
DOI：
10.1109/tkde.2016.2598171
发表时间：
2015-02
影响因子：
8.9
作者：
Jiajun Liu;Kun Zhao;P. Sommer;Shuo Shang;Branislav Kusy;Jae-Gil Lee;R. Jurdak
通讯作者：
Jiajun Liu;Kun Zhao;P. Sommer;Shuo Shang;Branislav Kusy;Jae-Gil Lee;R. Jurdak
DOI：
10.5311/josis.2012.4.62
发表时间：
2012-06
期刊：
J. Spatial Inf. Sci.
影响因子：
--
作者：
Kai-Florian Richter;Falko Schmid;P. Laube
通讯作者：
Kai-Florian Richter;Falko Schmid;P. Laube
充分利用车辆航向的三阶段在线地图匹配算法
DOI：
10.1007/s12652-018-0760-0
发表时间：
2018-10-01
影响因子：
--
作者：
Chen, Chao;Ding, Yan;Zhang, Shu
通讯作者：
Zhang, Shu
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1109/tkde.2017.2776927
发表时间：
2018-04
影响因子：
8.9
作者：
Xiaochun Yang;Bin Wang;Kai Yang;Chengfei Liu;Baihua Zheng
通讯作者：
Xiaochun Yang;Bin Wang;Kai Yang;Chengfei Liu;Baihua Zheng