Predicting Traffic and Risk Exposure in the Maritime Industry
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
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| Series: | Safety |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-576X/5/3/42 |
| _version_ | 1818297372349825024 |
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| author | Stephen Vander Hoorn Sabine Knapp |
| author_facet | Stephen Vander Hoorn Sabine Knapp |
| author_sort | Stephen Vander Hoorn |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T04:18:23Z |
| format | Article |
| id | doaj.art-e2e30f9f1ae74441aad7545586b8e0f2 |
| institution | Directory Open Access Journal |
| issn | 2313-576X |
| language | English |
| last_indexed | 2024-12-13T04:18:23Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Safety |
| spelling | doaj.art-e2e30f9f1ae74441aad7545586b8e0f22022-12-21T23:59:49ZengMDPI AGSafety2313-576X2019-07-01534210.3390/safety5030042safety5030042Predicting Traffic and Risk Exposure in the Maritime IndustryStephen Vander Hoorn0Sabine Knapp1School of Population and Global Health, University of Western Australia, Perth 6009, AustraliaEconometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 Rotterdam, The NetherlandsMaritime 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.https://www.mdpi.com/2313-576X/5/3/42risk assessmentbinary logistic regressionspatial statisticsincident modelsuncertaintiesmonetary value at riskincident consequences |
| spellingShingle | Stephen Vander Hoorn Sabine Knapp Predicting Traffic and Risk Exposure in the Maritime Industry Safety risk assessment binary logistic regression spatial statistics incident models uncertainties monetary value at risk incident consequences |
| title | Predicting Traffic and Risk Exposure in the Maritime Industry |
| title_full | Predicting Traffic and Risk Exposure in the Maritime Industry |
| title_fullStr | Predicting Traffic and Risk Exposure in the Maritime Industry |
| title_full_unstemmed | Predicting Traffic and Risk Exposure in the Maritime Industry |
| title_short | Predicting Traffic and Risk Exposure in the Maritime Industry |
| title_sort | predicting traffic and risk exposure in the maritime industry |
| topic | risk assessment binary logistic regression spatial statistics incident models uncertainties monetary value at risk incident consequences |
| url | https://www.mdpi.com/2313-576X/5/3/42 |
| work_keys_str_mv | AT stephenvanderhoorn predictingtrafficandriskexposureinthemaritimeindustry AT sabineknapp predictingtrafficandriskexposureinthemaritimeindustry |