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

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.

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

“…Dong, Yuan [62] present a novel algorithm of direction-aware continuous moving K-nearest neighbor queries in road networks. They showed how object azimuth information can be used to determine the moving direction towards the query object.…”

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

“…Researchers developed algorithms and index structures to evaluate spatial queries, including NN and RN queries for LBSs [ 6 , 22 , 23 , 24 , 25 ]. When calculating the length of the shortest path between two points, spatial queries for dynamic spatial networks suffer from high computational cost because graph traversal is required at runtime.…”

“…When calculating the length of the shortest path between two points, spatial queries for dynamic spatial networks suffer from high computational cost because graph traversal is required at runtime. Therefore, numerous studies attempt to reduce the computational cost of the shortest path distance, to avoid unnecessary shortest-path computations [ 6 , 22 , 23 , 24 , 25 ]. Incremental Euclidean restriction (IER) and incremental network expansion (INE) were developed for NN queries [ 3 ].…”

“…A scalable and in-memory k NN query processing method called SIMkNN [ 26 ] was developed to quickly evaluate snapshot k NN queries over moving objects in a spatial network. The existing methods described in [ 6 , 22 , 23 , 24 , 25 ] are considered to be one-query-at-a-time processing algorithms because they aim to quickly evaluate a spatial query rather than a batch of spatial queries. This study is motivated by the observation that, with simple modifications, NN query processing algorithms can be applied to evaluate RN queries for spatial networks.…”

Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark

“…In a recent work, the authors study the problem of CkNN queries on moving objects to retrieve the k-NNs of all points along a query trajectory in spatial road networks [23]. A novel direction-aware CkNN algorithm, the so-called DACKNN, is recommended.…”