Location Selection of Public Transit Transfer Hubs Based on Gray NN Model Improved by GA

Min An Tang; Xiao Ming Wang; Shuang Yuan; Zhen Rong Sun

doi:10.4028/www.scientific.net/AMR.660.174

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 660Location Selection of Public Transit Transfer Hubs...

Location Selection of Public Transit Transfer Hubs Based on Gray NN Model Improved by GA

Abstract:

The public traffic flow has the gray characteristics of “small sample and poor information”, thereby a forecast method for transfer flow based on the gray soft computing is proposed. This method utilizes the gray system theory to establish gray neural network prediction model, aiming to improve performance of the neural network as well as the accuracy of the system’s prediction by using genetic algorithm. The results show that the optimized model can more accurately predict the traffic flow, providing a more effective way of location selection for public transit transfer hubs. Finally, take the planning of public transit transfer hubs in Lanzhou City as an example to carry out empirical analysis and evaluation for the transfer hubs using this method

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Periodical:

Advanced Materials Research (Volume 660)

Pages:

174-178

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.660.174

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Online since:

February 2013

Authors:

Min An Tang, Xiao Ming Wang, Shuang Yuan, Zhen Rong Sun

Keywords:

Artificial Neural Network (ANN), Genetic Algorithm (GA), Gray System Theory, Hub of Public Transport Transfer, Intelligence Traffic System, Location Selection

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References

[1] M. A. Tang, in: Location Selection for Urban Road Traffic Resources Based on Soft Computing. Lanzhou, Doctoral Dissertation of Lanzhou Jiaotong University. Lanzhou, P.R. China(2011), pp.60-61.

DOI: 10.14257/astl.2014.45.01

Google Scholar

[2] B. Zhang, W. Deng: Journal of Transportation Systems Engineering and Information Technology. Vol. 10, No. 4(2010), pp.155-160.

Google Scholar

[3] X. M. Zhou, D. Di and D. Wu: Journal of Tongji University(Natural Science). Vol. 39, No. 9(2011), pp.1313-1317, 1358.

Google Scholar

[4] C. M. Zhu, G. M. Hu and Y. D. Liu: Journal of Chongqing Jiaotong University. Vol. 25, No. 3(2006), pp.95-98.

Google Scholar

[5] J. L. Deng: The Journal of Gray System. Vol. 1, No. 1(1989), pp.1-24.

Google Scholar

[6] S. Y. Chen, W. Wang: Journal of Southeast University (Natural Science Edition). Vol. 34, No. 4( 2004), pp.541-544.

Google Scholar

[7] L Zhong, in: Artificial Neural Network and its Fusion Technology, edited by Science Press, Beijing(2007), in press.

Google Scholar

[8] S. Y. Ge, O. S. Jia and H. Liu: Power System Technology. Vol. 36, No. 1(2012), pp.224-229.

Google Scholar