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Probabilistic Assessment of Vessel Collision Risk: An Evidential Reasoning and Artificial Potential Field-Based Method

In: Multi-Criteria Decision Making in Maritime Studies and Logistics

Author

Listed:
  • Feng Ma

    (Wuhan University of Technology
    National Engineering Research Center of Water Transportation Safety (WTS))

  • Yu-Wang Chen

    (The University of Manchester)

Abstract

This chapter proposes a novel method to estimate the collision probabilities of monitoring targets for coastal radar surveillance. Initially, the probability of a monitoring target being a real moving vessel is estimated using the records of manual operations and the Evidential Reasoning (ER) rule. Subsequently, the bridges, piers and other encountering vessels in a waterway are characterized as collision potential fields using an Artificial Potential Field (APF) model, and the corresponding coefficients can be trained in terms of the historical vessel distributions. As a result, the positional collision potential of any monitoring vessel can be obtained through overlapping all the collision potential fields together. The probabilities of authenticity and the collision potential are further formulated as two pieces of evidence on which the Dempster’s rule of combination is used to reason the collision probability of a monitoring target. The vessels associated with high collision probabilities can be highlighted for supervisors’ attention, as they potentially pose high risks to safety. A preliminary field test was conducted to validate the proposed method.

Suggested Citation

  • Feng Ma & Yu-Wang Chen, 2018. "Probabilistic Assessment of Vessel Collision Risk: An Evidential Reasoning and Artificial Potential Field-Based Method," International Series in Operations Research & Management Science, in: Paul Tae-Woo Lee & Zaili Yang (ed.), Multi-Criteria Decision Making in Maritime Studies and Logistics, chapter 0, pages 123-149, Springer.
  • Handle: RePEc:spr:isochp:978-3-319-62338-2_6
    DOI: 10.1007/978-3-319-62338-2_6
    as

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