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Classification and prediction of port variables using Bayesian Networks

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  • Molina Serrano, Beatriz
  • González-Cancelas, Nicoleta
  • Soler-Flores, Francisco
  • Camarero-Orive, Alberto

Abstract

Many variables are included in planning and management of port terminals. They can be economic, social, environmental and institutional. Agent needs to know relationship between these variables to modify planning conditions. Use of Bayesian Networks allows for classifying, predicting and diagnosing these variables. Bayesian Networks allow for estimating subsequent probability of unknown variables, basing on know variables.

Suggested Citation

  • Molina Serrano, Beatriz & González-Cancelas, Nicoleta & Soler-Flores, Francisco & Camarero-Orive, Alberto, 2018. "Classification and prediction of port variables using Bayesian Networks," Transport Policy, Elsevier, vol. 67(C), pages 57-66.
    • Handle: RePEc:eee:trapol:v:67:y:2018:i:c:p:57-66
    DOI: 10.1016/j.tranpol.2017.07.013
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    References listed on IDEAS

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    1. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    2. Janssens, Davy & Wets, Geert & Brijs, Tom & Vanhoof, Koen & Arentze, Theo & Timmermans, Harry, 2006. "Integrating Bayesian networks and decision trees in a sequential rule-based transportation model," European Journal of Operational Research, Elsevier, vol. 175(1), pages 16-34, November.
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    Cited by:

    1. Majid Eskafi & Milad Kowsari & Ali Dastgheib & Gudmundur F. Ulfarsson & Gunnar Stefansson & Poonam Taneja & Ragnheidur I. Thorarinsdottir, 2021. "A model for port throughput forecasting using Bayesian estimation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 348-368, June.
    2. Yang, Zaili & Yang, Zhisen & Smith, John & Robert, Bostock Adam Peter, 2021. "Risk analysis of bicycle accidents: A Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

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