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A probabilistic cellular automaton to forecast port choice decisions

    Mabel A. Leva Affiliation
    ; Alejandro León Affiliation
    ; Rodrigo A. Garrido Affiliation
    ; Ángel Ibeas Affiliation

Abstract

The port choice problem consists in predicting the selection of a port, made by an agent who has alternatives to choose from. Most of the literature has tackled this problem assuming a discrete choice model dependent on the ports’ characteristics and agents’ attributes. However, in practice the port choice decision depends also on the choices made by other agents as well as decisions made by these agents in the past. There are only a few examples that incorporate the complexity generated by spatio-temporal interactions between agents. However, those modelling structures are rather cumbersome, precluding their use in practical cases. This article presents a new modelling framework to predict port choice decisions, based on the theory of Cellular Automaton (CA), which is simple in structure and can be quickly calibrated and applied. This framework is a probabilistic CA intended to imitate the decision processes made from multiple shippers that interact with each other. These shippers face similar alternatives of seaports for exporting their products within a certain time span. The port choice here is a dynamic decision that depends on the ports’ characteristics and attributes of each shipper at a given time, as well as the decisions made by their neighbours. The outcome of the interaction is a discrete decision that evolves in time according to the dynamics of the system as a whole. The specified CA was applied to the case of vehicle exports from Brazil and the calibration was performed through a genetic algorithm. The results show that the probabilistic CA is able to replicate the historic behaviour of the port choice decisions in the Brazilian vehicle industry, with a high degree of success. The spatial component of the CA turned out to be of major relevance in the dynamic decision process along with the attributes and geographical location of ports.

Keyword : port choice, cellular automaton, probabilistic, port competition, hinterland formation, discrete choice

How to Cite
Leva, M. A., León, A., Garrido, R. A., & Ibeas, Ángel. (2018). A probabilistic cellular automaton to forecast port choice decisions. Transport, 33(3), 801-809. https://doi.org/10.3846/transport.2018.5478
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Sep 28, 2018
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References

ANFAVEA. 2016. Brazilian Automotive Industry Yearbook. Associação Nacional dos Fabricantes de Veículos Automotores (ANFAVEA). 152 p.

Chopard, B.; Droz, M. 2005. Cellular Automata Modeling of Physical Systems. Cambridge University Press. 356 p.

Chou, C.-C.; Chu, C.-W.; Liang, G.-S. 2008. A modified regression model for forecasting the volumes of Taiwan’s import containers, Mathematical and Computer Modelling 47(9–10): 797–807. https://doi.org/10.1016/j.mcm.2007.05.005

Garrido, R. A.; Leva, M. 2004. Port of destination and carrier selection for fruit exports: a multi-dimensional space–time multinomial probit model, Transportation Research Part B: Methodological 38(7): 657–667. https://doi.org/10.1016/j.trb.2003.10.001

Garrido, R. A.; Mahmassani, H. S. 2000. Forecasting freight transportation demand with the space–time multinomial probit model, Transportation Research Part B: Methodological 34(5): 403–418. https://doi.org/10.1016/S0191-2615(99)00032-6

Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional. 432 p.

Goles, E.; Martínez, S. 2010. Cellular Automata and Complex Systems. Springer. 182 p.

Griffeath, D.; Moore, C. 2003. New Constructions in Cellular Automata. Oxford University Press. 360 p.

Hausman, J.; McFadden, D. 1984. Specification tests for the multinomial logit model, Econometrica 52(5): 1219–1240. https://doi.org/10.2307/1910997

Lee, S.-Y.; Chang, Y.-T.; Lee, P. T.-W. 2010. Container port selection factors: heterogeneity among major market players, Journal of International Logistics and Trade 8(2): 73–90. https://doi.org/10.24006/jilt.2010.8.2.004

Magala, M.; Sammons, A. 2008. A new approach to port choice modelling, Maritime Economics & Logistics 10(1–2): 9–34. https://doi.org/10.1057/palgrave.mel.9100189

Malchow, M.; Kanafani, A. 2004. A disaggregate analysis of port selection, Transportation Research Part E: Logistics and Transportation Review 40(4): 317–337. https://doi.org/10.1016/j.tre.2003.05.001

Malchow, M.; Kanafani, A. 2001. A disaggregate analysis of factors influencing port selection, Maritime Policy & Management: The Flagship Journal of International Shipping and Port Research 28(3): 265–277. https://doi.org/10.1080/03088830110060840

Manski, C. F.; McFadden, D. 1981. Structural Analysis of Discrete Data with Econometric Applications. The MIT Press. 504 p.

Moya, J. M.; Feo Valero, M. 2017. Port choice in container market: a literature review, Transport Reviews 37(3): 300–321. https://doi.org/10.1080/01441647.2016.1231233

Papola, A. 2004. Some developments on the cross-nested logit model, Transportation Research Part B: Methodological 38(9): 833–851. https://doi.org/10.1016/j.trb.2003.11.001

Song, D.-W.; Yeo, K.-T. 2004. A competitive analysis of Chinese container ports using the analytic hierarchy process, Maritime Economics & Logistics 6(1): 34–52. https://doi.org/10.1057/palgrave.mel.9100096

Tiwari, P.; Itoh, H.; Doi, M. 2003. Shippers’ port and carrier se-lection behaviour in China: a discrete choice analysis, Mari-time Economics & Logistics 5(1): 23–39. https://doi.org/10.1057/palgrave.mel.9100062

Tongzon, J. L. 2009. Port choice and freight forwarders, Transportation Research Part E: Logistics and Transportation Review 45(1): 186–195. https://doi.org/10.1016/j.tre.2008.02.004

Tongzon, J. L.; Sawant, L. 2007. Port choice in a competitive environment: from the shipping lines’ perspective, Applied Economics 39(4): 477–492. https://doi.org/10.1080/00036840500438871

Van Canneyt, M.; Gärtner, M.; Heinig, S.; De Carvalho, F. M.; Ouedraogo, I. 2011. Lazarus: The Complete Guide. Blaise Pas-cal Magazine. 736 p.

Veldman, S.; Garcia-Alonso, L.; Liu, M. 2016. Testing port choice models using physical and monetary data: a comparative case study for the Spanish container trades, Maritime Policy & Management: The Flagship Journal of International Shipping and Port Research 43(4): 495–508. https://doi.org/10.1080/03088839.2015.1099754

Veldman, S.; Garcia-Alonso, L.; Vallejo-Pinto, J. Á. 2013. A port choice model with logit models: a case study for the Spanish container trade, International Journal of Shipping and Trans-port Logistics 5(4/5): 373–389. https://doi.org/10.1504/IJSTL.2013.055277

Yeo, G.-T.; Ng, A. K. Y.; Lee, P. T.-W.; Yang, Z. 2014. Modelling port choice in an uncertain environment, Maritime Policy &Management: The Flagship Journal of International Shipping and Port Research 41(3): 251–267. https://doi.org/10.1080/03088839.2013.839515