Temporally enhanced network-constrained (TENC) R-tree

Abstract

This paper describes a new Network-constrained Moving objects indexing structure, which extends the state-of-the-art for this kind of data. The indexing structure we propose is called Temporally Enhanced Network-Constrained R-tree (TENC R-tree), which solves the shortcomings in other Network-Constrained access methods like the FNR-tree [7], MON-tree [1] and UTR-tree. These existing indexing methods are designed to store and retrieve the moving objects based on spatial features, followed by their temporal ones. They are generally not efficient when a query has only temporal constraints, or when a specific moving object id is also part of the query conditions. In such cases, existing methods have to scan the entire database to retrieve the result. Furthermore, the aforementioned methods are not efficient in processing Strict-path query, which is a query type that retrieves trajectories that follow all the edges in the queried path [10].Our proposed TENC R-tree index allows good performance for almost all types of queries on moving objects in a constrained network, whether the constraints are spatial, temporal, or based on object id. Also, the TENC R-tree out-performs other access methods on the case of Path queries. Our experiments show the performance has been improved by 10 to 100 times for such queries.