Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)

Utilize Improved Particle Swarm to Predict Traffic Flow

Authors
Hongying Liu
Corresponding Author
Hongying Liu
Available Online May 2014.
DOI
10.2991/iccia.2012.343How to use a DOI?
Keywords
Improved particle swarm, RBF neural network, Traffic flow prediction
Abstract

Presented an improved particle swarm optimization algorithm, introduced a crossover operation for the particle location, interfered the particles’ speed, made inert particles escape the local optimum points, enhanced PSO algorithm's ability to break away from local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and test results showed that, the improved algorithm can effetely forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better can be effectively applied to actual traffic control.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
Series
Advances in Intelligent Systems Research
Publication Date
May 2014
ISBN
978-94-91216-41-1
ISSN
1951-6851
DOI
10.2991/iccia.2012.343How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hongying Liu
PY  - 2014/05
DA  - 2014/05
TI  - Utilize Improved Particle Swarm to Predict Traffic Flow
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 1381
EP  - 1384
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.343
DO  - 10.2991/iccia.2012.343
ID  - Liu2014/05
ER  -