Research On Spatio-temporal Index For Urban Traffic Network

Recently, constraint-network-based spatio-temporal data management has been a new research topic in spatio-temporal data management. The spatio-temporal index for the constraint network is one of the key issues in this field. For most applications involving a huge amount of moving objects, such as urban traffic management, building efficient spatio-temporal index is the most important way to suit the efficiency requirements of the applications.In this paper, we take urban traffic network as the background, summarize the existing network data model, provide the relevant definitions and propose the improved data model of urban traffic network. At the same time, we present a novel spatio-temporal index for constraint network, which is called NBR-tree (Network-Based R-tree), and we focus on the background of the applications in an urban traffic network. The NBR-tree is an improvement on the previous index named MON-tree, with an analysis on the specific properties of the moving objects in an urban traffic network. Finally, we compare the performance of the NBR-tree and the MON-tree index through the experiment.The main contribution of this paper can be summed up as follows:(1) We summarize the existing network data model, improve and consummate the Güting road network data model, provide the urban traffic network and the complete definitions of object (including moving object and static object);(2) We present a novel spatio-temporal index for constraint network: NBR-tree. The NBR-tree is an improvement on the previous index named MON-tree, with an analysis on the specific properties of the moving objects in an urban traffic network. We discuss the index structure, operating algorithms as well as the experiments of the NBR-tree in detail;(3) The compared experiments are carried on the data set from Brinkhoff's moving object generator. The experimental results show that our proposed index is able to support NN queries, window queries and queries given start positions and directions, and is more efficient than the MON-tree in evaluating trajectory queries.