A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

Rapidly growing GPS (Global Positioning System) trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsu...

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Vydáno v:ISPRS international journal of geo-information Ročník 7; číslo 5; s. 164
Hlavní autoři: Zhou, Xiangbing, Miao, Fang, Ma, Hongjiang, Zhang, Hua, Gong, Huaming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2018
Témata:
Algorithms
angle-based line segments partitioning
Clustering
Global positioning systems
GPS
Hausdorff distance
Lagrange-based fuzzy C-means
least squares
Least squares method
least squares regression
Mathematical models
Migration
Partitioning
planning
Positioning systems
Regression models
Robustness (mathematics)
Satellite navigation systems
Stability
taxi GPS data
Trajectories
trajectory regression clustering
travel
Travel demand
ISSN:2220-9964, 2220-9964
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Shrnutí:Rapidly growing GPS (Global Positioning System) trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method) is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning) method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM) clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM), our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.
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ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi7050164