Passenger Train Delay Classification

Masoud Yaghini, Maryam Setayesh Sanai and Hossein Amin Sadrabady
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Masoud Yaghini: Department of Railway Transportation Engineering, School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
Maryam Setayesh Sanai: Department of Railway Transportation Engineering, School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
Hossein Amin Sadrabady: Research and Training Center of Iranian Railways, Tehran, Iran

International Journal of Applied Metaheuristic Computing (IJAMC), 2013, vol. 4, issue 1, 21-31

Abstract: One of the most popular data mining areas, which estimate future trends of data, is classification. This research is dedicated to predict Iranian passenger train delay with high accuracy over Iranian railway network. A hybrid method based on neuro-fuzzy inference system and Two-step clustering is used for this purpose. The results indicate that the hybrid method is superior over the other common classification methods. The result can be used by train dispatcher to accurate schedule trains to diminish train delay average.

Date: 2013
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