Short-time Traffic Flow Prediction Method Based on Universal Organic Computing Architecture

DOI

10.2991/iccia.2012.303How to use a DOI?

Keywords

Short-time traffic flow, Organic computing, Particle Swarm Optimization algorithm, RBF Neural network

Abstract

Designed a DNA-based genetic algorithm under the universal architecture of organic computing, combined particle swarm optimization algorithm, introduced a crossover operation for the particle location, can interfere with the particles’ speed, make inert particles escape the local optimum points, enhanced PSO algorithm's ability to get rid of 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 error analysis of experimental results showed that, the designed algorithms can accurately forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better, can be effectively applied to actual traffic engineering.

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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TY  - CONF
AU  - Shuzhi Nie
AU  - Yanhua Zhong
AU  - Ming Hu
PY  - 2014/05
DA  - 2014/05
TI  - Short-time Traffic Flow Prediction Method Based on Universal Organic Computing Architecture
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 1226
EP  - 1229
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.303
DO  - 10.2991/iccia.2012.303
ID  - Nie2014/05
ER  -