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

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.

Department(s)

Computer Science

Second Department

Electrical and Computer Engineering

Sponsor(s)

National Science Foundation (U.S.)

Comments

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

Keywords and Phrases

Expert systems; Motor transportation; Query processing; Traffic control; Effectiveness and efficiencies; Empirical studies; On currents; On The Go; Traffic conditions; Traffic estimation; Traffic prediction; Traffic congestion

International Standard Book Number (ISBN)

978-3-662-46484-7

International Standard Serial Number (ISSN)

0302-9743; 1611-3349

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Springer Verlag, All rights reserved.

Publication Date

01 Feb 2015

Link to Full Text

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