GPS Trajectory Clustering and Visualization Analysis

Li Cai (), Sijin Li (), Shipu Wang () and Yu Liang ()
Additional contact information
Li Cai: Fudan University
Sijin Li: Yunnan University
Shipu Wang: Yunnan University
Yu Liang: Yunnan University

Annals of Data Science, 2018, vol. 5, issue 1, No 4, 29-42

Abstract: Abstract The trajectory data of taxies containing time dimensional and spatial dimensional information is an important kind of traffic data. How to obtain valuable information from these data has become a hot topic in the field of intelligent transportation. Existing trajectory clustering algorithms can only compute similarities using partial characteristics of the trajectory data, leading to clustering results are not accurate. This study proposes a novel trajectory clustering algorithm named GLTC, which can obtain more accurate number of clusters based on the global and local characteristics of trajectories. This study intuitively displays the laws and knowledge in clustering results using visualization techniques. Experimental results reveal that the GLTC algorithm can discover more accurate clustering results, effectively display spatial-temporal change trends in GPS data, and better assist in analyzing the flow law of urban citizens and urban traffic conditions using visualization methods.

Keywords: Trajectory clustering; GPS trajectory data; Visualization; Global characteristics; Local characteristics (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40745-017-0131-2

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