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

Author

Listed:
  • Ali Azadeh
  • Azam Boskabadi
  • Shima Pashapour

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).

Suggested Citation

  • Ali Azadeh & Azam Boskabadi & Shima Pashapour, 2015. "A unique support vector regression for improved modelling and forecasting of short-term gasoline consumption in railway systems," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 21(2), pages 217-237.
    • Handle: RePEc:ids:ijsoma:v:21:y:2015:i:2:p:217-237
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    Citations

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    Cited by:

    1. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    2. Mirpouya Mirmozaffari & Gohar Azeem & Azam Boskabadi & Ali Aranizadeh & Aditya Vaishnav & Jithin John, 2020. "A Novel Improved Data Envelopment Analysis Model Based on SBM and FDH Models," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(3), May.
    3. Mitra Khaksar & Mir Mohammad Ali Malakoutian, 2020. "Productivity Evaluation for Banking System in Developing Countries: DEA Malmquist Productivity Index Based on CCR, BCC, CCR-BCC (A Case Study)," Post-Print hal-03221338, HAL.
    4. Hossein Kamalzadeh & Saeid Nassim Sobhan & Azam Boskabadi & Mohsen Hatami & Amin Gharehyakheh, 2019. "Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines," Papers 1912.02373, arXiv.org.
    5. Ali Aranizadeh & Mehrzad Kazemi & Homayoun Barahmandpour & Hamidreza Ahady Dolatsara, 2020. "AHP Decision Making Algorithm for Development of HVDC and EHVAC in Developing Countries," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(3), May.
    6. Mirpouya Mirmozaffari & Azam Boskabadi & Gohar Azeem & Reza Massah & Elahe Boskabadi & Hamidreza Ahady Dolatsara & Ata Liravian, 2020. "Machine learning Clustering Algorithms Based on the DEA Optimization Approach for Banking System in Developing Countries," European Journal of Engineering and Technology Research, European Open Science, vol. 5(6), pages 651-658, June.

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