Distance and Bearing Based Vehicle Trajectory Segmentation

Mirge, Vaishali; Verma, Kimmi

doi:10.26438/ijcse/v7i4.677681

Cited by 3 publications

(2 citation statements)

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“…Finally, SPD was verified on the geolife dataset. Mirge and Verma define the distance threshold and the angle threshold to segment trajectory [13]. Although these two algorithms can quickly find the stay points and segment the trajectory, the algorithm requires heuristic rules.…”

Section: Literature Reviewmentioning

confidence: 99%

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Application of Artificial Intelligence in an Unsupervised Algorithm for Trajectory Segmentation Based on Multiple Motion Features

Dong

2022

Wireless Communications and Mobile Computing

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With the development of the wireless network, location-based services (e.g., the place of interest recommendation) play a crucial role in daily life. However, the data acquired is noisy, massive, it is difficult to mine it by artificial intelligence algorithm. One of the fundamental problems of trajectory knowledge discovery is trajectory segmentation. Reasonable segmentation can reduce computing resources and improvement of storage effectiveness. In this work, we propose an unsupervised algorithm for trajectory segmentation based on multiple motion features (TS-MF). The proposed algorithm consists of two steps: segmentation and mergence. The segmentation part uses the Pearson coefficient to measure the similarity of adjacent trajectory points and extract the segmentation points from a global perspective. The merging part optimizes the minimum description length (MDL) value by merging local sub-trajectories, which can avoid excessive segmentation and improve the accuracy of trajectory segmentation. To demonstrate the effectiveness of the proposed algorithm, experiments are conducted on two real datasets. Evaluations of the algorithm’s performance in comparison with the state-of-the-art indicate the proposed method achieves the highest harmonic average of purity and coverage.

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Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, the proposed trajectory segmentation algorithms can be classified the supervised [9][10][11][12][13][14], unsupervised [15][16][17][18][19][20][21][22][23][24][25][26][27], and semisupervised [28].…”

Section: Introductionmentioning

confidence: 99%