A unique support vector regression for improved modelling and forecasting of short-term gasoline consumption in railway systems

Ali Azadeh, Azam Boskabadi and Shima Pashapour

International Journal of Services and Operations Management, 2015, vol. 21, issue 2, 217-237

Abstract: This study presents a support vector regression algorithm and time series framework to estimate and predict weekly gasoline consumption in railway transportation industry. For training support vector machines, recursive finite Newton (RFN) algorithm is used. Furthermore, it considers the effect of number of holidays per weeks and amount of transported freight and number of transported passengers in gasoline consumption prediction. Transported passengers per kilometre and transported tons per kilometre are the most important factors in railway industry. For this reason, this study assesses the effect of these factors on weekly gasoline consumption. Weekly gasoline consumption in railway transportation industry of Iran from August 2009 to December 2011 is considered. It is shown that SVR achieves better results in comparison with other intelligent tools such as artificial neural network (ANN).

Keywords: support vector regression; SVR; gasoline consumption; petrol consumption; forecasting; railway systems; artificial neural networks; ANNs; modelling; railways; rail transport; support vector machines; SVM; recursive finite Newton; RFN; holidays per week; freight transport; passenger transport. (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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