Abstract
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.
Highlights
There are many LBS applications, such as taxi hailing, ride sharing and car navigation, and various K-nearest-neighbor (KNN) query algorithms have been proposed to solve these problems
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
When the moving object reaches a road network node, an adjacent edge of the node is randomly selected as its traveling edge, the moving object will travel along the selected edge
Summary
There are many LBS applications, such as taxi hailing, ride sharing and car navigation, and various K-nearest-neighbor (KNN) query algorithms have been proposed to solve these problems. The moving objects change their locations and directions frequently over time; the cost of retrieving the exact results of continuous K-nearest neighbor (CKNN) for moving objects is expensive, in highly dynamic spatio-temporal applications, for example, finding the nearest taxi while the user moves in road networks over a period of time. This paper proposes a continuous KNN query method for predicting the K-nearest moving objects via predictive computation. There aree ssoommee sscchhoollaarrss [[44––66]] tthhaatt ccoonnssiiddeerr tthhee ddiirreeccttiioonn--aawwaarree qquueerryy ooff mmoovviinngg oobbjjeeccttss iinn EEuucclliiddeeaann ssppaaccee. H(2e) dTihsteadnicsetabnectewbeetnwteweon otwbjoecotsbjiescrtespirserseepnrtesdenbytetdhbeyshthoerteshstoprtaetsht bpeattwh ebeentwtheeentwthoeotbwjeoctosbijnectthseinrotahde nroetawdonrekt.wo(3r)k.T(h3e) Trehseurltessueltt isneteiancheascuhbs-uinbt-eirnvtearlvoafl aofpaerpieordioids idsedteertmerimneinde.d.TThheefifrirssttpprroobblleemm ttoo bbee ssoollvveedd iiss tthhee ccaallccuullaattiioonn ooff tthhee ddiissttaanncceess bbeettwweeeenn mmoovviinng oobbjjeeccts aanndd tthhee qquueerryy oobbjjeecctt aatt eevveerryy ttiimmeessttaammpp iinn tthhee rrooaadd nneettwwoorrkk. Because of the continuous movement of objects in the road network, tthhee ddiissttaannccee bbeettwweeeenn aannyy ttwwoo oobbjjeeccttss iiss ccoonnssttaannttllyy cchhaannggiinngg.
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