Predictive Line Queries for Traffic Prediction
Abstract
The advances in communication and positioning device technologies have made it possible to track the locations of moving objects, such as vehicles equipped with GPS. as a result, a new series of applications and services have been commenced into people's life. One popular application is the real-time traffic system which provides current road condition and traffic jam information to commuters. to further enhance this location-Based experience, this paper proposes an advanced type of service which can predict traffic jams so that commuters can plan their trips more effectively. in particular, traffic prediction is realized by a new type of query, termed as the predictive line query, which estimates the 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 RD -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 work in terms of both accuracy and time efficiency. © 2012 Springer-Verlag.
Recommended Citation
L. Heendaliya et al., "Predictive Line Queries for Traffic Prediction," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7600 LNCS, pp. 106 - 133, Springer, Dec 2012.
The definitive version is available at https://doi.org/10.1007/978-3-642-34179-3_4
Department(s)
Computer Science
Second Department
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-364234178-6
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
27 Dec 2012
