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Research on Dynamic Traffic Flow Forecasting Based on Improved Particle Swarm Optimization Algorithm and Neural Network Theory

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Abstract:

Focused on urban traffic flow issue, the dynamic traffic flow forecasting model is established based on the improved particle optimization algorithm and nerve network theory. Taking the macro dynamic traffic flow as the model, the paper analyzes primarily the features of traffic flow by means of stage-distinguishing method, researches the improved particle optimization algorithm and nerve network theory further and establishes the dynamic traffic flow forecasting model. Finally, it utilizes this model to forecast the traffic flow on North Binghe Road in Lanzhou City. All the results demonstrate that this forecasting model is of higher prestige and proper availability.

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Periodical:

Applied Mechanics and Materials (Volumes 178-181)

Pages:

2686-2689

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.178-181.2686

Citation:

Cite this paper

Online since:

May 2012

Authors:

Bo Qi, Chang Xi Ma, Li Sun

Keywords:

Dynamic Traffic Flow, Forecasting Model, Nerve Network, Particle Optimization Algorithm

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] Papageorgiou M, B bsseville J M. Modeling and real-time control of traffic flow on the southern part of boulevard peripherique in Paris. Transportation Research, Vol24(1990),P.345-359.

DOI: 10.1016/0191-2607(90)90047-a

Google Scholar

[2] D.J. Beebe, J.S. Moore, J.M. Bauer , Q. Yu , R.H. Liu, C. Devados and B. Jo: Nature Vol. 404 (2000), p.588

Google Scholar

[3] E. Birgersson, H. Li and S. Wu: J. Mech. Phys. Solids Vol. 56 (2008), p.444

Google Scholar

[4] S. Hirotsu: J. Chem. Phys. Vol. 94 (1991), p.3949

Google Scholar

[5] Ma Changxi. Hazardous Material Highway Transportation Route Multi-attribute Decision- making Based on Neural Network Theory under Undeveloped Transport Network Environment. Modern Traffic Technology,Vol.6(2009),pp.84-86.

Google Scholar

[6] Ma Changxi. Highway Transportation Route Decision-making Model of Hazardous Material under Uncertain Linguistic Environment. Journal of Wuhan University of Technology(Transportation Science and Engineering), Vol.34(2010),pp.916-919.

Google Scholar

[7] Information on http://www.tranbbs.com.

Google Scholar