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Continuous Predictive Line Queries for On-the-Go Traffic Estimation

  • Lasanthi HeendaliyaEmail author
  • Dan Lin
  • Ali Hurson
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8980)

Abstract

Traffic condition is one vital piece of information that any commuter would wish to obtain to plan an efficient route. However, most existing works monitor and report only current traffic, which makes it too late for commuters to change their routes when they realize they are already stuck in the traffic. Therefore, in this paper, we propose a traffic prediction approach by defining and solving a novel continuous predictive line query. The continuous predictive line query aims to accurately estimate traffic conditions in the near future based on current movement of vehicles on the roads, and continuously update the predicted traffic conditions as vehicles move. The predicted traffic condition will not only help redirect commuters in advance but also help relieve the overall traffic congestion problem. We have proposed three algorithms to answer the query and carried out both theoretical and empirical study. Our experimental results demonstrate the effectiveness and efficiency of our approach.

Notes

Acknowledgement

This work is partly funded by the U.S. National Science Foundation under Grant No. CNS-1250327.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceMissouri University of Science and TechnologyRollaUSA

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