Passenger Capacity of Underground Metro by the Use of Neural Network Program (NNP)
Mai M Eldeeb1, Akram S kotb2, Hany S Riad3, Ayman A. Ashour4

1Mai Moaz Eldeeb, civil department, higher technological institute 10th of Ramadan city,
2Akram soltan kotb, construction building, faculty of engineering and technology Arab academy for science, technology and maritime transport,Cairo
3Hany Sobhy Riad, Civil Eng. Dept. Ain Shams University Cairo,
4Mohamed Ayman Ashour, architecture Eng. Dept. Ain Shams University Cairo

Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2469-2473 | Volume-8 Issue-10, August 2019 | Retrieval Number: J95380881019/2019©BEIESP | DOI: 10.35940/ijitee.J9538.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The present paper deals with studying on (GCUM) by the use of (NNP), consequently, the future passenger fluctuations can be well predicted and it will be helpful to make wise decisions for realizing the most safety and economic future operation.To attain this goal, a methodology was proposed to collect the necessary data and analyze them. These data were applied as the inputs into the Neural Network Program (NNP) for the two (GCUM) lines ‘1’ & ‘2’ to have two models as inputs and outputs, one for the 1st line and the other for the 2nd one, taking only into consideration, the input, and output variables which gave tolerances less 19% than that were obtained by applying excel program. Thus, it is easily to predict the future capacity for any predicted year, and the corresponding headway as well as to prepare an estimated schedule complies with the required future Rolling Stock (RS).
Keywords: Greater Cairo Underground Metro (GCUM), metro schedule; annual fluctuations of metro passengers, Neural Network Program (NNP), Rolling Stock (RS)
Scope of the Article: Neural Network