Passenger Train Delay Classification

被引：0

作者：

Yaghini, Masoud ^{[1
]}

Sanai, Maryam Setayesh ^{[1
]}

Sadrabady, Hossein Amin ^{[2
]}

机构：

[1] Iran Univ Sci & Technol, Sch Railway Engn, Dept Rail Transportat Engn, Tehran, Iran

[2] Res & Training Ctr Iranian Railways, Tehran, Iran

来源：

INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING | 2013年 / 4卷 / 01期

关键词：

Classification; Iranian Railway Network; Neuro-Fuzzy; Passenger Train Delay; Two-step Clustering;

D O I：

10.4018/jamc.2013010102

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：21 / 31

页数：11

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