Presentation information

Interactive Session

[3Rin4] Interactive 1

Thu. Jun 11, 2020 1:40 PM - 3:20 PM Room R01 (jsai2020online-2-33)

[3Rin4-79] A Case Study of Stream and Density-Based Clustering for Floating Car Data

〇Mitsuki Kimura1, Shigeyuki Odashima1, Masashi Toyoda2 (1.Honda R&D Co., Ltd., 2.Institute of Industrial Science, The University of Tokyo)

Keywords:Time Series Data Analysis, Clustering, Location Data, ITS

Extracting stay points from location data is important for the detection of point-of-interests or facilities. In this
paper, we develop a method to extract stay points via density-based clustering algorithms, by using positional
data gathered from floating cars (FCD). Through our analysis, we found a characteristic of FCD when extracting
stay-points: the extracted clusters are highly affected by the space-time range of input data. We discuss this
characteristic and future directions by showing the clustering results from real data.

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