2019
DOI: 10.1155/2019/3518705
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Metrics for Assessing Overall Performance of Inland Waterway Ports: A Bayesian Network Based Approach

Abstract: Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports… Show more

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Cited by 28 publications
(11 citation statements)
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References 44 publications
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“…The mutual information method identifies the important variables that should be used in Bayesian models, and thus it improves the accuracy of model results (Yang, Yang, and Yin 2018) as it accounts for model uncertainties. The Bayesian method has been used in the literature in different fields including ship emissions (Liu and Duru 2020), shipping accidents (Zhang and Thai 2016), resilience of inland waterways ports (Hosseini and Barker 2016), deep-water port infrastructure resilience (Hossain et al 2019), and classification of port variables (Molina-Serrano et al 2018). However, the application of a Bayesian method to forecast port throughput is scant in the scientific literature.…”
Section: Different Port Throughput Forecasting Methodsmentioning
confidence: 99%
“…The mutual information method identifies the important variables that should be used in Bayesian models, and thus it improves the accuracy of model results (Yang, Yang, and Yin 2018) as it accounts for model uncertainties. The Bayesian method has been used in the literature in different fields including ship emissions (Liu and Duru 2020), shipping accidents (Zhang and Thai 2016), resilience of inland waterways ports (Hosseini and Barker 2016), deep-water port infrastructure resilience (Hossain et al 2019), and classification of port variables (Molina-Serrano et al 2018). However, the application of a Bayesian method to forecast port throughput is scant in the scientific literature.…”
Section: Different Port Throughput Forecasting Methodsmentioning
confidence: 99%
“…To further explore the versatility of Bayesian networks in multiple areas, readers should consider the works of Perez-Minaña [70], (natural resource management); Zhou et al [71] (safety risk analysis); Hossain et al [72] and Hossain et al [73] (waterway port); Neil et al [74] (legal arguments); Kabir et al [75]; Hossain et al [76] (supply chain); Ghosh et al [77] (project management),;Hossain et al [78] (electrical infrastructure); Hosseini and Sardar [79] (electric vehicle); Shin et al [80] (cyber risk); Saldao et al [81] (information dependencies); Alipio et al [82] (vehicle traffic and flood monitoring); Goyal and Chanda [83] (financial institution); Hossain et al [84]; and several others.…”
Section: Fundamentals Of Bayesian Network and Its Applicationmentioning
confidence: 99%
“…Their work consists of a formulation for supplier selection, that accounts for operational (e.g., customer demand) and disruption (e.g., natural disaster) risks and their effect on resilient suppliers. (Hossain et al 2019b) applied Bayesian Network to rank ports infrastructure assets. Their study proposes a formulation for supplier selection, accounting for operational (e.g., customer demand) and disruption (e.g., natural disaster) risks and their effect on resilient suppliers.…”
Section: Introductionmentioning
confidence: 99%
“…Their study proposes a formulation for supplier selection, accounting for operational (e.g., customer demand) and disruption (e.g., natural disaster) risks and their effect on resilient suppliers. (Hossain et al 2019b) introduced a BN approach to rank port infrastructure assets. (Eldosouky et al 2017) introduced a novel analytical resilience index to measure the effect of each critical infrastructure's physical component on its probability of failure using Bayesian Network.…”
Section: Introductionmentioning
confidence: 99%