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

Shu Zhi Nie; Yan Hua Zhong; Ming Hu

doi:10.4028/www.scientific.net/AMR.756-759.2785

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Short-Time Traffic Flow Prediction Method Based on...

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

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.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

2785-2789

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.2785

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Online since:

September 2013

Authors:

Shu Zhi Nie, Yan Hua Zhong, Ming Hu

Keywords:

Organic Computing, Particle Swarm Optimization Algorithm, RBF Neural Network, Short-Time Traffic Flow

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References

[1] Stevanovic A, Stevanovic J, Kergaye C, Martin P. Traffic control optimization for multi-modal operations in a large-scale urban network. Integrated and Sustainable Transportation System (FISTS), 2011 IEEE Forum on. 2011: 146-151.

DOI: 10.1109/fists.2011.5973603

Google Scholar

[2] Alexander Scheidler, Arne Brutschy, Konrad Diwold, Daniel Merkle and Martin Middendorf. Ant Inspired Methods for Organic Computing. Autonomic Systems. Vol. 1(1), 2011: 95-109.

DOI: 10.1007/978-3-0348-0130-0_6

Google Scholar

[3] C. Han. Short-time traffic flow prediction research. Master's degree thesis of North China University of technology. 2012: 11-13.

Google Scholar

[4] G.J. Shen, X.H. Wang, X.J. Kong. Short-term traffic volume intelligent hybrid forecasting model and its application. Systems Engineering-Theory & Practice. Vol. 31(3), 2011: 561-568.

Google Scholar

[5] S.Z. Nie, Yanhua ZHONG. Multi-objective Flexible Scheduling Optimization Scheme base on Improved DNA Genetic Algorithm. Journal of Computers. Vol. 7, No. 8, 2012: 1982-(1989).

DOI: 10.4304/jcp.7.8.1982-1989

Google Scholar