نوع مقاله : علمی - پژوهشی
نویسندگان
1 دانشگاه علم و صنعت ایران
2 گروه مهندسی حمل و نقل ریلی، دانشکده مهندسی راه آهن، دانشگاه علم و صنعت ایران
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسنده [English]
The aim of this paper is to predict passenger train delays in Iranian Railways using data mining techniques. The results of this research are used to design train timetables. The data used in this research includes a database of passenger train delays from year 2013 to 2018, including 380,748 records. Independent variables for prediction model include year, month, day, day of the week, departure time, axis, train type, car type, origin and destination of the train and the train owner. In order to model prediction of train delay, two kind of prediction, named Numerical and Classification are used on entire database. Neural network and C5.0 methods are used for classification prediction. The Twostep clustering method is used to divide the delay field into three labels. Regression, CHAID and neural network methods are used for numerical prediction. To evaluate prediction results, we divide existing data set into two subsets called training set and test set, in which delays from year 2013 to 2016 are the training set and delays of year 2017 are the test set. By evaluating the prediction methods, the results show that in numerical prediction, neural network method and in prediction by classification, C5.0 method has higher accuracy than other methods. Therefore, these two techniques have been used to predict the train delays of year 2018. Numerical prediction is used by grouping some database fields. The results show that the prediction by grouping has higher accuracy than the prediction on the entire database.
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