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A PSO-GRNN model for railway freight volume prediction: Empirical study from China

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2014
DOI: 
http://dx.doi.org/10.3926/jiem.1007

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Purpose: In this study, we aim to design a mathematical model for railway freight volume prediction which can provide an accurate direction for railway freight resource allocation. Based on the precise prediction, railway freight enterprises are able to optimize and integrate the limited resources to organize freight transport more efficiently and economically. Design/methodology/approach: In this study, we design a PSO-GRNN model to predict railway freight volume. In the proposed model, GRNN carries out the nonlinear regression analysis between railway freight volume and its influencing factors which can be described by detailed and measurable indexes and outputs the prediction value. PSO algorithm with time linear decreasing inertia weight and time varying acceleration coefficients is applied to optimize the basic GRNN model by searching the optimal smoothing parameter. Findings: The simulation result in this study indicates that (1) the PSO-GRNN model is able to predict railway freight volume by using the value of other relevant indexes as its input to fit the variation of railway freight volume; (2) Through optimization for GRNN model based on PSO algorithm, the proposed model performs well prediction accuracy; (3) Compared with RBFNN model and BPNN model, the superiority of the proposed model in prediction accuracy and curve fitting capacity is verified.Originality/value: In the empirical study from China, we establish a railway freight volume prediction index system containing seventeen prediction indexes from five influencing factors. Then we construct the PSO-GRNN model to predict the railway freight volume from 2007 to 2011 by using data from 1990 to 2006 as training samples. In order to verify the feasibility and prediction accuracy of the proposed model, we also use two widely applied neural network models, including RBFNN model and BPNN model, to estimate the railway freight volume prediction during the same period. Compared with RBFNN model and BPNN model, the simulation results indicate that the proposed model has higher prediction accuracy and better curve fitting capacity. Therefore, PSO-GRNN model can be further applied in practical railway freight production.
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REFERENCES

References: 

Adeli, H., & Panakkat, A. (2009). A probabilistic neural network for earthquake magnitude
prediction. Neural Networks, 22(7), 1018-1024. http://dx.doi.org/10.1016/j.neunet.2009.05.003
Brahme, A., Winning, M., & Raabe, D. (2009). Prediction of cold rolling texture of steels using
an Artificial Neural Network. Computational Materials Science, 46(4), 800-804.
http://dx.doi.org/10.1016/j.commatsci.2009.04.014
Chen, C., & Vachtsevanos, G. (2012). Bearing condition prediction considering uncertainty: an
interval type-2 fuzzy neural network approach. Robotics and Computer-Integrated
Manufacturing, 28(4), 509-516. http://dx.doi.org/10.1016/j.rcim.2012.02.005
Gnana Sheela, K., & Deepa, S.N. (2013). Neural network based hybrid computing model for
wind speed prediction. Neurocomputing, 122, 425-429.
http://dx.doi.org/10.1016/j.neucom.2013.06.008
Hashemi, A.B., & Meybodi, M.R. (2004). A note on the learning automata based algorithms for
adaptive parameter selection in PSO. Applied Soft Computing Journal, 11(1), 689-705.
http://dx.doi.org/10.1016/j.asoc.2009.12.030
Leeghim, H., Seo, I., & Bang, H. (2008). Adaptive nonlinear control using input normalized
neural networks. Journal of Mechanical Science and Technology, 22(6), 1073-1083.
http://dx.doi.org/10.1007/s12206-007-1119-1
Hou, Y., Lu, L., Xiong, X., Cheng, S., & Wu, Y. (2004). Enhanced particle swarm optimization
algorithm and ITS application on economic dispatch of power system. Proceedings of The
Chinese Society for Electrical Engineering, 24(7), 95-100.
Kennedy, J., & Eberhart, R.C. (1995). Particle swarm optimization. 1995 IEEE International
Conference on Neural Networks Proceedings, Perth, WA., (AUSTRALIA), 27 Nov.-1 Dec.,
1995, available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=488968
Korres, D.M., Anastopoulos, G., Lois, E., Alexandridis, A., Sarimveis, H., & Bafas, G. (2002). A
neural network approach to the prediction of diesel fuel lubricity. Fuel, 81(10), 1243-1250.
http://dx.doi.org/10.1016/S0016-2361(02)00020-0
Liang, T., & Noore, A. (2004). Software reliability prediction using recurrent neural network
with Bayesian regularization. International Journal of Neural Systems, 14(3), 165-174.
http://dx.doi.org/10.1142/S0129065704001966
Liu, Z., Ji, L., Ye, Y., & Geng, Z. (2006). A study on prediction of railway freight volumes based
on RBF neural network. Journal of the China Railway Society, 28(5), 1-5.
López-Martín, C., Isaza C., & Chavoya, A. (2012). Software development effort prediction of
industrial projects applying a general regression neural network. Empirical Software
Engineering, 17(6), 738-756. http://dx.doi.org/10.1007/s10664-011-9192-6
Mostafa, M.M. (2012). Forecasting stock exchange movements using neural networks:
Empirical evidence from Kuwait. Expert Systems with Applications, 37(9), 6302-6309.
http://dx.doi.org/10.1016/j.eswa.2010.02.091
Nie, P., Geng, J., & Qin, Z. (2011). Self-adaptive inertia weight PSO test case generation
algorithm considering prematurity restraining. International Journal of Digital Content
Technology and its Applications, 5(9), 125-133. http://dx.doi.org/10.4156/jdcta.vol5.issue9.14
Okkan, U. (2012). Wavelet neural network model for reservoir inflow prediction. Scientia
Iranica, 19(6), 1445-1455. http://dx.doi.org/10.1016/j.scient.2012.10.009
Ömer Faruk, D. (2010). A hybrid neural network and ARIMA model for water quality time
series prediction. Engineering Applications of Artificial Intelligence, 23(4), 586-594.
http://dx.doi.org/10.1016/j.engappai.2009.09.015
Parzen, E. (1962). On the estimation of a probability density function and mode. Annals of
Mathematical Statistics, 33(3), 1065-1076. http://dx.doi.org/10.1214/aoms/1177704472
Ratnaweera, A., Halgamuge S.K., & Watson, H.C. (2004). Self-organizing hierarchical particle
swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on
Evolutionary Computation, 8(3), 240-255. http://dx.doi.org/10.1109/TEVC.2004.826071
Specht, D.F. (1991). A general regression neural network. IEEE Transactions on Neural
Networks, 2(6), 568-576. http://dx.doi.org/10.1109/72.97934
Tsuda, I. (1992). Dynamic link of memory-chaotic memory map in nonequilibrium neural
networks. Neural Networks, 5(2), 313-326. http://dx.doi.org/10.1016/S0893-6080(05)80029-2
Wang, Z., Wang, Y., Jia, L., & Li, P. (2005). Application of improved BP neural network in the
prediction of railway passenger volume time serial. China Railway Science, 26(2), 127-131.

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