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

被引：9

作者：

Dong, Tianyang ^{[1
]}

Yuan, Lulu ^{[1
]}

Shang, Yuehui ^{[1
]}

Ye, Yang ^{[1
]}

Zhang, Ling ^{[2
]}

机构：

[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China

[2] Zhejiang Univ Sci & Technol, Sch Econ & Management, Hangzhou 310023, Zhejiang, Peoples R China

来源：

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION | 2019年 / 8卷 / 09期

基金：

中国国家自然科学基金;

关键词：

direction-aware; road network; moving objects; continuous K nearest neighbor query; OBJECTS; SEARCH;

D O I：

10.3390/ijgi8090379

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

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