Volume List  / Volume 9 (3)

Article

FUZZY LOGIC APPLICATION IN GREEN TRANSPORT - PREDICTION OF FREIGHT TRAIN ENERGY CONSUMPTION

DOI: 10.7708/ijtte.2019.9(3).04


9 / 3 / 299-309 Pages

Author(s)

Jovana Ćalić - University of Belgrade, Faculty of Transport and Traffic Engineering, Belgrade, Serbia -

Milica Šelmić - University of Belgrade, Faculty of Transport and Traffic Engineering, Belgrade, Serbia -

Dragana Macura - University of Belgrade, Faculty of Transport and Traffic Engineering, Belgrade, Serbia -

Miloš Nikolić - University of Belgrade, Faculty of Transport and Traffic Engineering, Belgrade, Serbia -


Abstract

Rail freight transport is one of the most preferred modes of green transport since it emits three times less CO2 and particulates per ton-mile than road transport. Train energy consumption is the biggest issue related to rail traction costs. Data about freight trains energy consumption per year are not possible to define precisely, so it is convenient to use fuzzy logic as a tool for data prediction. In order to predict it, we provide Wang - Mendel method for combining both numerical and linguistic information into a common framework – a fuzzy rule base. Relevant input variables are: freight train kilometers, average freight trains weight and non-productive kilometers. The output variable from the defined fuzzy logic system is average energy consumption per year for rail freight transport. The proposed model is applied and tested on real data collected in the Republic of Serbia.


Download Article

Number of downloads: 29


Acknowledgements:

This work has been supported by Serbian Ministry of Education, Science and Technological Development through grants No. TR36002 and TR36022.


References:

Ćalić, J. 2018. Prognoza veličine i strukture teretnog kolskog parka za Srbija Kargo a.d. [In English: Volume and structure forecast of freight wagon fleet for Serbia Cargo a. d.]. BSc. thesis. University in Belgrade, Faculty of transport and traffic engineering, Belgrade.

 

CE Delft, Infras, Fraunhofer ISI. 2011. External Costs of Transport in Europe: Update Study for 2008. Report commissioned by International Union of Railways UIC, CE Delft, Delft.

 

Chen, D.; Zhang, J.; Jia, Sh. 2007. WM method and its application in traffic flow modeling. In Proceedings of the International Conference on Transportation Engineering, 339-345.

 

Jozi, A.; Pinto, T.; Praça, I.; Ramos, S.; Vale, Z.; Goujon, B.; Petrisor, T. 2017. Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study, In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-5.

 

Teodorović, D. 1999. Fuzzy logic systems for transportation engineering: the state of the art, Transportation Research Part A 33: 337-364.

 

Teodorović, D.; Šelmić, M. 2012. Računarska inteligencija u saobraćaju [In English: Computer intelligence in traffic], University in Belgrade, Faculty of transport and traffic engineering, Belgrade.

 

Wang, L.; Mendel, J. 1992. Generating fuzzy rules by learning from examples, IEEE Transactions on systems, man and cybernetics 22(6): 1414-1427.

 

Wang, L.X. 2003. The WM method completed: A flexible fuzzy system approach to data mining, IEEE Transactions on Fuzzy Systems 11(6): 768–782.

 

Yanar, T.A.; Akyurek, Z. 2011. Fuzzy model tuning using simulated annealing, Expert Systems with Applications 38: 8159-8169.

 

Yang, X.; Yuan, J.; Yuan, J.; Mao, H. 2010. An improved WM method based on PSO for electric load forecasting, Expert Systems with Applications 37: 8036-8041.


Quoted IJTTE Works



Related Keywords