A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Peng, Hao; Yao, Chunlong; Meng, Qingbin; Yu, Xiaoqiang; Xu, Li

doi:10.15388/informatica.2019.196

Cited by 53 publications

(6 citation statements)

References 59 publications

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“…Ref. [13] proposes a heading maintaining oriented trajectory compression algorithm, which takes into account both position information and direction information. Its idea is similar to that of normal opening window algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The fundamental goal of trajectory data compression is to extract trajectory feature points. It can be divided into two categories: Offline compression [2][3][4][5] and online compression [3,[6][7][8][9][10][11][12][13][14][15]. Offline compression means that the entire trajectory is compressed only after all the trajectory points has been collected, generally using global characteristics of the trajectory data for static compression processing.…”

Section: Introductionmentioning

confidence: 99%

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Online vehicle trajectory compression algorithm based on motion pattern recognition

Zhao

2022

IET Intelligent Trans Sys

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With the popularity of various portable mobile devices with positioning functions, a large amount of spatial‐temporal trajectory data has emerged. To effectively compress large‐scale vehicle trajectory data and serve intelligent transportation system, we propose a spatial‐temporal trajectory data compression algorithm based on vehicle motion pattern recognition. The algorithm recognizes vehicles' turning behaviour and variable speed behaviour during the driving process through the analysis of vehicle motion patterns, and extracts the trajectory's turning feature points and variable speed feature points, so as to achieve online compression of the vehicle trajectory. By means of large‐scale experimental datasets, the authors compare this algorithm with representative trajectory compression algorithms in various performance metrics. The experimental results indicate that as an online vehicle trajectory compression algorithm, the proposed algorithm is superior to representative compression algorithms in compression accuracy and computational efficiency, and the compression results can reflect semantic motion information such as turning patterns and variable speed patterns in the trajectory. Therefore, this algorithm has comprehensive advantages, which is foundational for trajectory data mining in intelligent transportation systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online vehicle trajectory compression algorithm based on motion pattern recognition

Zhao

2022

IET Intelligent Trans Sys

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“…Speed error [25] is a vital metric for various kinds of traffic applications. It measures the difference between the actual speed and the estimated speed.…”

Section: Speedmentioning

confidence: 99%

Velocity‐Preserving Trajectory Compression Based on Retrace Point Detection

Chen

Liu

2021

Wireless Communications and Mobile Computing

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With the increasing development of GPS-equipped mobile devices such as smart phones and vehicle navigation systems, the trajectories containing valuable spatiotemporal information are recorded. Typically, plenty of trajectory records are generated and stored, making the device memory suffer a heavy storage pressure. Thus, it is a vital issue to compress the trajectories. The trajectory semantics are usually ignored or reduced in traditional trajectory compression techniques. In addition, most of existing trajectory compression algorithms only concern the position errors rather than the velocity errors of trajectories. This paper proposes a velocity-preserving trajectory compression algorithm based on retrace point detection (VPTC-RP) that can compress a set of trajectories by removing unnecessary redundancy points, while the skeleton of these trajectories is maintained as much as possible. In VPTC-RP, the retrace points and the velocity errors are taken to reflect the speeds and directions attached with the points. VPTC-RP first determines the retrace points based on the changed movement directions, and then, the retrace points are extracted from the original trajectories. Especially, the retrace points are put in a buffer, and the subtrajectories in the buffer are compressed according to the measured velocity errors. Simulations are carried out on the Geolife trajectory dataset, and the simulation results indicate that VPTC-RP can achieve a preferable tradeoff among the compression error, compression ratio, and running time.

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“…Taking common taxi trajectory data as an example, if trajectory data are collected every 2–3 s, a single car can generate about 15,000 trajectory points a day. With 67,000 taxis in Beijing, for example, 1 day’s taxi trajectory data in Beijing takes up about 60 TB of memory ( Hao et al, 2019 ). The sheer volume of data that is being transmitted and stored takes a huge amount of time, and the amount of time it takes to analyze that data is a frightening number.…”

Section: Introductionmentioning

confidence: 99%

A trajectory data compression algorithm based on spatio-temporal characteristics

Zhong

Kong

Zhang

et al. 2022

PeerJ Computer Science

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Background With the growth of trajectory data, the large amount of data causes a lot of problems with storage, analysis, mining, etc. Most of the traditional trajectory data compression methods are focused on preserving spatial characteristic information and pay little attention to other temporal information on trajectory data, such as speed change points or stop points. Methods A data compression algorithm based on the spatio-temporal characteristics (CASC) of the trajectory data is proposed to solve this problem. This algorithm compresses trajectory data by taking the azimuth difference, velocity difference and time interval as parameters in order to preserve spatial-temporal characteristics. Microsoft’s Geolife1.3 data set was used for a compression test to verify the validity of the algorithm. The compression results were compared with the traditional Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR) and Opening Window (OPW) algorithms. Compression rate, the direction information of trajectory points, vertical synchronization distance, and algorithm type (online/offline) were used to evaluate the above algorithms. Results The experimental results show that with the same compression rate, the ability of the CASC to retain the forward direction trajectory is optimal, followed by TD-TR, DP, and then OPW. The velocity characteristics of the trajectories are also stably retained when the speed threshold value is not more than 100%. Unlike the DP and TD-TR algorithms, CASC is an online algorithm. Compared with OPW, which is also an online algorithm, CASC has better compression quality. The error distributions of the four algorithms have been compared, and CASC is the most stable algorithm. Taken together, CASC outperforms DP, TD-TR and OPW in trajectory compression.

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A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Cited by 53 publications

References 59 publications

Online vehicle trajectory compression algorithm based on motion pattern recognition

Online vehicle trajectory compression algorithm based on motion pattern recognition

Velocity‐Preserving Trajectory Compression Based on Retrace Point Detection

A trajectory data compression algorithm based on spatio-temporal characteristics

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