Optimizing Predictive Queries on Moving Objects under Road-Network Constraints

Advanced wireless communication and positioning technology has enabled a new series of applications, such as the intelligent traffic management system. It can be envisioned that the traffic management systems will have a great impact on our daily life in the near future. This paper aims to tackle one class of queries to be supported by such systems, predictive line queries. The predictive line query estimates amount of vehicles entering a querying road segment at a specified future timestamp and helps query issuers adjust their travel plans in a timely manner. Only a handful of existing work can efficiently and effectively handle such queries since most methods are designed for objects moving freely in the Euclidean space instead of under road-network constraints. Taking the road network topology and object moving patterns into account, we propose a hybrid index structure, the R-tree, which employs an R*-tree for network indexing and direction-based hash tables for managing vehicles. We also develop a ring-query-based algorithm to answer the predictive line query. We have conducted an extensive experimental study which demonstrates that our approach significantly outperforms existing works in terms of both accuracy and time efficiency.