Next Article in Journal
A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context
Next Article in Special Issue
A Hybrid Framework for High-Performance Modeling of Three-Dimensional Pipe Networks
Previous Article in Journal
A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System
Previous Article in Special Issue
The Distribution Pattern of the Railway Network in China at the County Level
Open AccessArticle

Direction-Aware Continuous Moving K-Nearest-Neighbor Query in Road Networks

1
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2
School of Economics and Management, Zhejiang University of Science and Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 379; https://doi.org/10.3390/ijgi8090379
Received: 29 May 2019 / Revised: 23 July 2019 / Accepted: 21 August 2019 / Published: 29 August 2019
Continuous K-nearest neighbor (CKNN) queries on moving objects retrieve the K-nearest neighbors of all points along a query trajectory. They mainly deal with the moving objects that are nearest to the moving user within a specified period of time. The existing methods of CKNN queries often recommend K objects to users based on distance, but they do not consider the moving directions of objects in a road network. Although a few CKNN query methods consider the movement directions of moving objects in Euclidean space, no efficient direction determination algorithm has been applied to CKNN queries over data streams in spatial road networks until now. In order to find the top K-nearest objects move towards the query object within a period of time, this paper presents a novel algorithm of direction-aware continuous moving K-nearest neighbor (DACKNN) queries in road networks. In this method, the objects’ azimuth information is adopted to determine the moving direction, ensuring the moving objects in the result set towards the query object. In addition, we evaluate the DACKNN query algorithm via comprehensive tests on the Los Angeles network TIGER/LINE data and compare DACKNN with other existing algorithms. The comparative test results demonstrate that our algorithm can perform the direction-aware CKNN query accurately and efficiently. View Full-Text
Keywords: direction-aware; road network; moving objects; continuous K nearest neighbor query direction-aware; road network; moving objects; continuous K nearest neighbor query
Show Figures

Figure 1

MDPI and ACS Style

Dong, T.; Yuan, L.; Shang, Y.; Ye, Y.; Zhang, L. Direction-Aware Continuous Moving K-Nearest-Neighbor Query in Road Networks. ISPRS Int. J. Geo-Inf. 2019, 8, 379.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop