Optimizing Predictive Queries on Moving Objects under Road-network Constraints
Abstract
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 D -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. © 2011 Springer-Verlag Berlin Heidelberg.
Recommended Citation
L. Heendaliya et al., "Optimizing Predictive Queries on Moving Objects under Road-network Constraints," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6860 LNCS, no. PART 1, pp. 247 - 261, Springer, Sep 2011.
The definitive version is available at https://doi.org/10.1007/978-3-642-23088-2_17
Department(s)
Computer Science
Second Department
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-364223087-5
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
20 Sep 2011