In this paper, we present ROAD, a general framework to evaluate Location-Dependent Spatial Queries (LDSQ)s that searches for spatial objects on road networks. By exploiting search space pruning technique and providing a dynamic object mapping mechanism, ROAD is very efficient and flexible for various types of queries, namely, range search and nearest neighbor search, on objects over large-scale networks. ROAD is named after its two components, namely, Route Overlay and Association Directory, designed to address the network traversal and object access aspects of the framework. In ROAD, a large road network is organized as a hierarchy of interconnected regional sub-networks (called Rnets) augmented with 1) shortcuts for accelerating network traversals; and 2) object abstracts for guiding traversals. In this paper, we present (i) the Rnet hierarchy and several properties useful to construct Rnet hierarchy, (ii) the design and implementation of the ROAD framework, (iii) efficient object search algorithms for various queries, and (iv) incremental update techniques for framework maintenance in presence of object and network changes. We conducted extensive experiments with real road networks to evaluate ROAD. The experiment result shows the superiority of ROAD over the state-of-the-art approaches.
For example, Lee et al. REF presented a general framework named ROAD to evaluate Location-Dependent Spatial Queries (LDSQ)s on road networks.
Fast object search on road networks
EDBT '09
Computer Science