Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82243
Title: Dynamic update and monitoring of aoi entrance via spatiotemporal clustering of drop-off points
Authors: Zhou, T 
Liu, XT 
Qian, Z
Chen, HX
Tao, F
Issue Date: 2019
Source: Sustainability, 3 Dec. 2019, v. 11, no. 23, 6870, p. 1-20
Abstract: This paper proposes a novel method for dynamically extracting and monitoring the entrances of areas of interest (AOIs). Most AOIs in China, such as buildings and communities, are enclosed by walls and are only accessible via one or more entrances. The entrances are not marked on most maps for route planning and navigation in an accurate way. In this work, the extraction scheme of the entrances is based on taxi trajectory data with a 30 s sampling time interval. After fine-grained data cleaning, the position accuracy of the drop-off points extracted from taxi trajectory data is guaranteed. Next, the location of the entrances is extracted, combining the density-based spatial clustering of applications with noise (DBSCAN) with the boundary of the AOI under the constraint of the road network. Based on the above processing, the dynamic update scheme of the entrance is designed. First, a time series analysis is conducted using the clusters of drop-off points within the adjacent AOI, and then, a relative heat index (RHI) is applied to detect the recent access status (closed or open) of the entrances. The results show the average accuracy of the current extraction algorithm is improved by 24.3% over the K-means algorithm, and the RHI can reduce the limitation of map symbols in describing the access status. The proposed scheme can, therefore, help optimize the dynamic visualization of the entry symbols in mobile navigation maps, and facilitate human travel behavior and way-finding, which is of great help to sustainable urban development.
Keywords: Area of interest (AOI)
Spatiotemporal clustering
Drop-off points of taxi
Update and monitoring of entrance
Machine learning
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sustainability 
EISSN: 2071-1050
DOI: 10.3390/su11236870
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Zhou, T.; Liu, X.; Qian, Z.; Chen, H.; Tao, F. Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points. Sustainability 2019, 11, 6870, 1-20 is available at https://dx.doi.org/10.3390/su11236870
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