Predictive tree: An efficient index for predictive queries on road networks

Abdeltawab M. Hendawi, Jie Bao, Mohamed F. Mokbel, Mohamed Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


Predictive queries on moving objects offer an important category of location-aware services based on the objects' expected future locations. A wide range of applications utilize this type of services, e.g., traffic management systems, location-based advertising, and ride sharing systems. This paper proposes a novel index structure, named Predictive tree (P-tree), for processing predictive queries against moving objects on road networks. The predictive tree: (1) provides a generic infrastructure for answering the common types of predictive queries including predictive point, range, KNN, and aggregate queries, (2) updates the probabilistic prediction of the object's future locations dynamically and incrementally as the object moves around on the road network, and (3) provides an extensible mechanism to customize the probability assignments of the object's expected future locations, with the help of user defined functions. The proposed index enables the evaluation of predictive queries in the absence of the objects' historical trajectories. Based solely on the connectivity of the road network graph and assuming that the object follows the shortest route to destination, the predictive tree determines the reachable nodes of a moving object within a specified time window T in the future. The predictive tree prunes the space around each moving object in order to reduce computation, and increase system efficiency. Tunable threshold parameters control the behavior of the predictive trees by trading the maximum prediction time and the details of the reported results on one side for the computation and memory overheads on the other side. The predictive tree is integrated in the context of the iRoad system in two different query processing modes, namely, the precomputed query result mode, and the on-demand query result mode. Extensive experimental results based on large scale real and synthetic datasets confirm that the predictive tree achieves better accuracy compared to the existing related work, and scales up to support a large number of moving objects and heavy predictive query workloads.

Original languageEnglish (US)
Title of host publication2015 IEEE 31st International Conference on Data Engineering, ICDE 2015
PublisherIEEE Computer Society
Number of pages12
ISBN (Electronic)9781479979639
StatePublished - May 26 2015
Event2015 31st IEEE International Conference on Data Engineering, ICDE 2015 - Seoul, Korea, Republic of
Duration: Apr 13 2015Apr 17 2015

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Other2015 31st IEEE International Conference on Data Engineering, ICDE 2015
Country/TerritoryKorea, Republic of

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


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