Next Article in Journal
A Case Study in the Application of the Systematic Approach to Training in the Logging Industry
Next Article in Special Issue
Leaders’ Influence Tactics for Safety: An Exploratory Study in the Maritime Context
Previous Article in Journal
ATC Separation Assurance for RPASs and Conventional Aircraft in En-Route Airspace
Open AccessArticle

Predicting Traffic and Risk Exposure in the Maritime Industry

1
School of Population and Global Health, University of Western Australia, Perth 6009, Australia
2
Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 Rotterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Safety 2019, 5(3), 42; https://doi.org/10.3390/safety5030042
Received: 26 March 2019 / Revised: 29 May 2019 / Accepted: 27 June 2019 / Published: 1 July 2019
(This article belongs to the Special Issue Maritime Safety and Operations)
Maritime regulators, port authorities, and industry require the ability to predict risk exposure of shipping activities at a micro and macro level to optimize asset allocation and to mitigate and prevent incidents. This article introduces the concept of a strategic planning tool by making use of the multi-layered risk estimation framework (MLREF), which accounts for ship specific risk, vessel traffic densities, and meets ocean conditions at the macro level. This article’s main contribution is to provide a traffic and risk exposure prediction routine that allows the traffic forecast to be distributed across the shipping route network to allow for predicting scenarios at the macro level (e.g., covering larger geographic areas) and micro level (e.g., passage way, particular route of interest). In addition, the micro level is introduced by providing a theoretical idea to integrate location specific spatial rate ratios along with the effect of the risk control option to perform sensitivity analysis of risk exposure prediction scenarios. Aspects of the risk exposure estimation routine were tested via a pilot study for the Australian region using a comprehensive and unique combination of datasets. Sources of uncertainties for risk assessments are described in general and discussed along with the potential for future developments and improvements. View Full-Text
Keywords: risk assessment; binary logistic regression; spatial statistics; incident models; uncertainties; monetary value at risk; incident consequences risk assessment; binary logistic regression; spatial statistics; incident models; uncertainties; monetary value at risk; incident consequences
Show Figures

Figure 1

MDPI and ACS Style

Vander Hoorn, S.; Knapp, S. Predicting Traffic and Risk Exposure in the Maritime Industry. Safety 2019, 5, 42.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop