DAVT: An Error-Bounded Vehicle Trajectory Data Representation and Compression Framework

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.