DETECTING HOTSPOTS FROM TAXI TRAJECTORY DATA USING SPATIAL CLUSTER ANALYSIS
A method of trajectory clustering based on decision graph and data field is proposed in this paper. The method utilizes data field to describe spatial distribution of trajectory points, and uses decision graph to discover cluster centres. It can automatically determine cluster parameters and is suit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-4-W2/131/2015/isprsannals-II-4-W2-131-2015.pdf |
| Summary: | A method of trajectory clustering based on decision graph and data field is proposed in this paper. The method utilizes data field to
describe spatial distribution of trajectory points, and uses decision graph to discover cluster centres. It can automatically determine
cluster parameters and is suitable to trajectory clustering. The method is applied to trajectory clustering on taxi trajectory data, which
are on the holiday (May 1<sup>st</sup>, 2014), weekday (Wednesday, May 7<sup>th</sup>, 2014) and weekend (Saturday, May 10<sup>th</sup>, 2014) respectively, in
Wuhan City, China. The hotspots in four hours (8:00-9:00, 12:00-13:00, 18:00-19:00 and 23:00-24:00) for three days are discovered
and visualized in heat maps. In the future, we will further research the spatiotemporal distribution and laws of these hotspots, and use
more data to carry out the experiments. |
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| ISSN: | 2194-9042 2194-9050 |