Abstract
The objective of this article is to predict volumes of Ro-Ro (Roll-on, Roll-off) freight in order to apply this prediction as a decision making tool in logistics planning and port organization. This tool can help to improve supply chain performance in a Ro-Ro terminal. Seasonal ARIMA (SARIMA) and Artificial Neural Networks (ANNs) were the forecasting methods used in this study. A resampling procedure was applied in order to find out the best model from a statistical point of view using multiple comparison methods. The results have been very promising (R=0.9157; d=0.9546; MSE=0.0195)
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References
Apivatanagul, P., Regan, A.C.: Long haul freight network design using shipper–carrier freight flow prediction: A California network improvement case study. Transportation Research Part E: Logistics and Transportation Review 46, 507–519 (2010)
Walkowiak, T., Mazurkiewicz, J.: Analysis of critical situations in discrete transport systems. In: Fourth International Conference on Dependability of Computer Systems, DepCos-RELCOMEX 2009, p. 364. IEEE (2009)
Bianco, L., La Bella, A.: Freight transport planning and logistics: Lucio Bianco and Agostino La Bella. Springer, New York (1988)
Hesse, M., Rodrigue, J.: The transport geography of logistics and freight distribution. Journal of Transport Geography 12, 171–184 (2004)
Peng, W.Y., Chu, C.W.: A comparison of univariate methods for forecasting container throughput volumes. Mathematical and Computer Modelling 50, 1045–1057 (2009)
Dougherty, M.: A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies 3, 247–260 (1995)
Schulze, P.M., Prinz, A.: Forecasting container transshipment in Germany. Applied Economics 41, 2809–2815 (2009)
Dias, J.C.Q., Calado, J.M.F., Mendonça, M.C.: The role of European «ro-ro» port terminals in the automotive supply chain management. Journal of Transport Geography 18, 116–124 (2010)
Al-Deek, H.M.: Use of vessel freight data to forecast heavy truck movements at seaports. Transportation Research Record: Journal of the Transportation Research Board 1804, 217–224 (2002)
Mostafa, M.M.: Forecasting the Suez Canal traffic: a neural network analysis. Maritime Policy & Management 31, 139–156 (2004)
Godfrey, G.A., Powell, W.B.: Adaptive estimation of daily demands with complex calendar effects for freight transportation. Transportation Research Part B: Methodological 34, 451–469 (2000)
Van Der Voort, M., Dougherty, M., Watson, S.: Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies 4, 307–318 (1996)
Chung, E., Rosalion, N.: Short term traffic flow prediction. In: 24th Australasian Transport Research Forum (ATRF), Hobart, Tasmania, Australia (2001)
Vlahogianni, E.I., Golias, J.C., Karlaftis, M.G.: Short-term traffic forecasting: Overview of objectives and methods. Transport Reviews 24, 533–557 (2004)
Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach. Transportation Research Part C: Emerging Technologies 13, 211–234 (2005)
Lan, J., Guo, M., Lu, H., Xiao, X.: Short-Term Traffic Flow Combination Forecast by Co-integration Theory. Journal of Transportation Systems Engineering and Information Technology 11, 71–75 (2011)
Cantarella, G.E., de Luca, S.: Multilayer feedforward networks for transportation mode choice analysis: An analysis and a comparison with random utility models. Transportation Research Part C: Emerging Technologies 13, 121–155 (2005)
Zhu, Y., Chen, Y., Geng, X., Liu, L.: Transport Modal Split of Commercial Sites Based on Artificial Neural Network. Journal of Transportation Systems Engineering and Information Technology 8, 86–91 (2008)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, Oakland (1976)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)
Rummelhart, D.E., McClelland, J.L.: PDP Research Group, Parallel Distributed Processing. Explorations in the Microstructure of Cognition, vol. 1, Foundations (1986)
Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5, 989–993 (1994)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1–30 (2006)
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López, J.A.M., Ruiz-Aguilar, J.J., Turias, I., Cerbán, M., Jiménez-Come, M.J. (2014). A Comparison of Forecasting Methods for Ro-Ro Traffic: A Case Study in the Strait of Gibraltar. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Proceedings of the Ninth International Conference on Dependability and Complex Systems DepCoS-RELCOMEX. June 30 – July 4, 2014, Brunów, Poland. Advances in Intelligent Systems and Computing, vol 286. Springer, Cham. https://doi.org/10.1007/978-3-319-07013-1_33
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DOI: https://doi.org/10.1007/978-3-319-07013-1_33
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