Latest revision as of 18:45, 28 January 2021

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.

Original document

The different versions of the original document can be found in:

• http://download.atlantis-press.com/php/download_paper.php?id=4251 under the license cc-by

• http://dx.doi.org/10.2991/iccia.2012.303

• https://www.atlantis-press.com/proceedings/iccia-12/4251,

https://academic.microsoft.com/#/detail/1985792420

• https://www.scientific.net/AMR.756-759.2785,

https://academic.microsoft.com/#/detail/2179078038

• https://www.scientific.net/AMR.756-759.2785.pdf,

http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2785

DOIS: 10.2991/iccia.2012.303 10.4028/www.scientific.net/amr.756-759.2785