Direction-aware KNN queries for moving objects in a road network

Dong, Ting; Yuan, Lulu; Cheng, Qiang; Cao, Bin; Fan, Jiulun

doi:10.1007/s11280-019-00657-1

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…It used the SCOB index to optimize searching or update processing time and conducted workload analysis to configure SCOB indices to maximize throughput. Tianyang et al [36] proposed a new algorithm for direction-aware KNN (DAKNN) searching of moving objects on a road network. This method used R-tree and a grid as the index structure, and the authors also proposed a novel local network extension method to quickly determine the direction of moving objects, reduce the communication costs, and simplify the calculation of the moving direction between moving objects and query points.…”

Section: Related Work About the K-nn Search Problem Ofmentioning

confidence: 99%

Tree Index Nearest Neighbor Search of Moving Objects along a Road Network

Jiang

Wei

et al. 2021

Wireless Communications and Mobile Computing

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With the widespread application of location-based service (LBS) technology in the urban Internet of Things, urban transportation has become a research hotspot. One key issue of urban transportation is the nearest neighbor search of moving objects along a road network. The fast-updating operations of moving objects along a road network suppress the query response time of urban services. Thus, a tree-indexed searching method is proposed to quickly find the answers to user-defined queries on frequently updating road networks. First, a novel index structure, called the double tree-hash index, is designed to reorganize the corresponding relationships of moving objects and road networks. Second, an index-enhanced search algorithm is proposed to quickly find the k -nearest neighbors of moving objects along the road network. Finally, an experiment shows that compared with state-of-the-art algorithms, our algorithm shows a significant improvement in search efficiency on frequently updating road networks.

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Section: Related Work About the K-nn Search Problem Ofmentioning

confidence: 99%

Tree Index Nearest Neighbor Search of Moving Objects along a Road Network

Jiang

Wei

et al. 2021

Wireless Communications and Mobile Computing

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“…Therefore, we propose a direction-aware continuous moving K-nearest-neighbor query algorithm in road networks that is based on progressive network expansion. Our previous research has solved the problem of Direction-aware KNN queries for moving objects in a road network (DAKNN) [27]. In this paper, we focused on the problem of continuous queries and the efficiency of continuous K-nearest-neighbor query for moving objects in road networks that are moving towards query objects.…”

Section: Direction-based Knn Querymentioning

confidence: 99%

“…The road network is composed of nodes and edges, which is represented by an undirected weighted graph, namely, G (N, E, W), where N represents the set of nodes and stores all node information of the road network; E represents the set of edges and stores the edges of the road network; W represents the weight of the edge, where the weight that is set by the system is the length of the edge [27]. In addition, the two endpoints of a specified edge are denoted by ns and ne.…”

Section: Data Structurementioning

confidence: 99%

“…In our algorithm, a data structure of memory is used to store edge, node and moving object information [27].…”

Section: Data Structurementioning

confidence: 99%

“…Case 2: the moving object and the query object are on the different edge. This paper adopts the method of Direction-aware K-Nearest Neighbor Query [27], in which road network expansion and azimuth angle are used to determine k neighbor objects moving toward the query point. The azimuth angle is used to determine if the direction of moving object is toward the query point, and the direction determination rule is the direction of road network expansion is opposite the direction towards the query object.…”

Section: Direction-aware K-nearest Neighbor Query For Moving Objectsmentioning

confidence: 99%

See 2 more Smart Citations

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

Dong

Yuan

Shang

et al. 2019

IJGI

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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.

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Shadow: Answering Why-Not Questions on Top-K Spatial Keyword Queries over Moving Objects

Zhang

Shu

et al. 2021

Database Systems for Advanced Applications

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Direction-aware KNN queries for moving objects in a road network

Cited by 10 publications

References 22 publications

Tree Index Nearest Neighbor Search of Moving Objects along a Road Network

Tree Index Nearest Neighbor Search of Moving Objects along a Road Network

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

Shadow: Answering Why-Not Questions on Top-K Spatial Keyword Queries over Moving Objects

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