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Safety management of waterway congestions under dynamic risk conditions—A case study of the Yangtze River

Yan, XP, Wan, CP, Zhang, D and Yang, ZL (2017) Safety management of waterway congestions under dynamic risk conditions—A case study of the Yangtze River. Applied Soft Computing Journal, 59. pp. 115-128. ISSN 1568-4946

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With the continuous increase of traffic volume in recent years, inland waterway transportation suffers more and more from congestion problems, which form a major impediment to its development. Thus, it is of great significance for the stakeholders and decision makers to address these congestion issues properly. Fuzzy Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) is widely used for solving Multiple Criteria Decision Making (MCDM) problems with ambiguity. When taking into account fuzzy TOPSIS, decisions are made in a static scenario with fixed weights assigned to the criteria. However, risk conditions usually vary in real-life cases, which will inevitably affect the preference ranking of the alternatives. To make flexible decisions according to the dynamics of congestion risks and to achieve a rational risk analysis for prioritising congestion risk control options (RCOs), the cost-benefit ratio (CBR) is used in this paper to reflect the change of risk conditions. The hybrid of CBR and fuzzy TOPSIS is illustrated by investigating the congestion risks of the Yangtze River. The ranking of RCOs varies depending on the scenarios with different congestion risk conditions. The research findings indicate that some RCOs (e.g. “Channel dredging and maintenance”, and “Prohibition of navigation”) are more cost effective in the situation of a high level of congestion risk, while the other RCOs (e.g. “Loading restriction”, and “Crew management and training”) are more beneficial in a relatively low congestion risk condition. The proposed methods and the evaluation results provide useful insights for effective safety management of the inland waterway congestions under dynamic risk conditions. © 2017 Elsevier B.V.

Item Type: Article
Uncontrolled Keywords: 0102 Applied Mathematics, 0801 Artificial Intelligence And Image Processing, 0806 Information Systems
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
T Technology > TC Hydraulic engineering. Ocean engineering
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: Elsevier
Date Deposited: 29 Jun 2017 08:58
Last Modified: 04 Sep 2021 11:23
DOI or ID number: 10.1016/j.asoc.2017.05.053
URI: https://researchonline.ljmu.ac.uk/id/eprint/6743
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