Utilize Improved Particle Swarm to Predict Traffic Flow

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/).

Download article (PDF)

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  -