A Semantics-Based Trajectory Segmentation Simplification Method

Liu, Minshi; He, Guanghui; Long, Yi

doi:10.1007/s41651-021-00088-5

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…Ref. [24] considers semantic features of a trajectory stop and proposes a simplified method for trajectory segmentation based on stop features, and finally simplifies the obtained segments.…”

Section: Semantic-based Trajectory Compression Methodsmentioning

confidence: 99%

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: Semantic-based Trajectory Compression Methodsmentioning

confidence: 99%

Online vehicle trajectory compression algorithm based on motion pattern recognition

Zhao

2022

IET Intelligent Trans Sys

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“…The used metrics to perform the measurements to the "GR Simplification" algorithm, considering the application scenario in road networks and disconnected (batch) environments, are the compression ratio rate and the margin of error [28,57,58]. For the comparison of the "GR Simplification" algorithm and TD-TR, the margin of error formula found in this paper [59] is used.…”

Section: Initial Diagnostics Of Batch Gps Trajectory Simplification A...mentioning

confidence: 99%

Batch Simplification Algorithm for Trajectories over Road Networks

Reyes,

Estrada,

Tolozano-Benites

et al. 2023

IJGI

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The steady increase in data generation by GPS systems poses storage challenges. Previous studies show the need to address trajectory compression. The demand for accuracy and the magnitude of data require effective compression strategies to reduce storage. It is posited that the combination of TD-TR simplification, Kalman noise reduction, and analysis of road network information will improve the compression ratio and margin of error. The GR algorithm is developed, integrating noise reduction and path compression techniques. Experiments are applied with trajectory data sets collected in the cities of California and Beijing. The GR algorithm outperforms similar algorithms in compression ratio and margin of error, improving storage efficiency by up to 89.090%. The combination of proposed techniques presents an efficient solution for GPS trajectory compression, allowing to improve storage in trajectory analysis applications.

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“…We hope to explore valuable patterns in space-time. The existing index method for vehicle trajectory data mostly adopts the trajectory segmenting method based on road segment or distance [3] , and the semantic connotation of trajectory segments is weak, and there is still a huge gap between the application requirements and actual retrieval results [4] . For example, "What is the trajectory from Chongming Island to People's Square?".…”

Section: Introductionmentioning

confidence: 99%