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Open AccessArticle

Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points

by Tong Zhou 1,2,3, Xintao Liu 2, Zhen Qian 1, Haoxuan Chen 1 and Fei Tao 1,3,*
1
School of Geographical Sciences, Nantong University, Nantong 226007, China
2
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
3
Key Laboratory of Virtual Geographical Environment, MOE, Nanjing Normal University, Nanjing 210046, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(23), 6870; https://doi.org/10.3390/su11236870
Received: 23 October 2019 / Revised: 28 November 2019 / Accepted: 29 November 2019 / Published: 3 December 2019
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 ( R H I ) 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 R H I 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. View Full-Text
Keywords: area of interest (AOI); spatiotemporal clustering; drop-off points of taxi; update and monitoring of entrance; machine learning area of interest (AOI); spatiotemporal clustering; drop-off points of taxi; update and monitoring of entrance; machine learning
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MDPI and ACS Style

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.

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