Cluster analysis of the competitiveness of container ports in Brazil

Cabral, Alexandra Maria Rios; Ramos, Francisco de Sousa

doi:10.1016/j.tra.2014.09.005

Cited by 43 publications

(13 citation statements)

References 11 publications

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“…The second type of clustering analysis involves transportation service nodes, which include ports, airports and railway stations. Cabral and Ramos [24] and Gianfranco et al [25] clustered container ports to analyse the relationships between them. Vogel and Graham [26], Cui et al [27] and Mayer [28] clustered airports, and explored the formation mechanism of airport groups and the relationships among airport categories, cargo types and geographical areas.…”

Section: Clustering Methods Used In the Transportation Fieldmentioning

confidence: 99%

A Two-Dimensional Clustering Method for High-Speed Railway Trains in China Based on Train Characteristics and Operational Performance

Deng

Chen

et al. 2020

IEEE Access

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At present, China has the world's longest high-speed railway (HSR) network, but most trains do not have clear market positioning and hierarchical standard. The current train hierarchies and adjustment is empirically set by HSR organisers. This paper applies a clustering method and cross-over analysis to study the classification of China's HSR trains and provides scientific operation suggestions. By adopting the timetable data and ticket booking data of Shanghai-Nanjing intercity railway, we establish a clustering index system consists of train characteristics indexes and operational performance indexes, then use t-distributed Stochastic Neighbour Embedding (t-SNE) to do dimensionality reduction for original data. We obtain the optimal clustering number by validity indexes and use k-means to cluster the HSR trains. After clustering, we use a cross-over analysis to illustrate the relationship between train characteristics, passenger demand and operational performance. We find there are two main types of train on Shanghai-Nanjing intercity HSR: trains departing on the hour that have good operational performance, trains with staggered stops and low-capacity based on the strategies of ''low capacity, high density'' that have better operational performance at peak time. After 19:00, due to the passenger demand decrease, train capacity can be reduced to avoid unnecessary waste. Short-distance trains could easily be replaced by long-distance trains with similar stop schedules and need to maintain a certain operation frequency. The proposed clustering method has universal applicability for Chinese HSR lines. INDEX TERMS High-speed railway train, clustering analysis, train characteristics, operational performance, cross-over analysis.

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Section: Clustering Methods Used In the Transportation Fieldmentioning

confidence: 99%

A Two-Dimensional Clustering Method for High-Speed Railway Trains in China Based on Train Characteristics and Operational Performance

Deng

Chen

et al. 2020

IEEE Access

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“…In this application, transferring the same principle to the evaluation of maritime transport chains, a comparative analysis of the origin/destination (O/D) connections that make up the two networks is first performed using a set of KPIs and then applying a factor-cluster analysis to produce clusters of observations based on the relevant KPIs. Clustering is one of the most popular statistical tools with a plethora of applications in many fields, including the maritime transport sector where it is mainly used to investigate the performance of ports and container terminals [9,22,60,72]. The goal of clustering is traditionally to find meaningful groups of observations so that the similarity among the elements in a cluster is greater than the similarity among different clusters.…”

Section: Background Literaturementioning

confidence: 99%

Performance assessment of alternative SSS networks by combining KPIs and factor-cluster analysis

Serra

Fancello

2020

Eur. Transp. Res. Rev.

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Performance assessment is a fundamental tool to successfully monitor and manage logistics and transport systems. In the field of Short Sea Shipping (SSS), the performance of the various maritime initiatives should be analyzed to assess the best way to achieve efficiency and guide related policies. This study proposes a quantitative methodology which can serve as a decision-support tool in the preliminary assessment and comparison of alternative SSS networks. The research is executed via a Mediterranean case study that compares a hypothetical Mediterranean ro-ro SSS network developed in the framework of a past Euro-Mediterranean cooperation project with the network of existing ro-ro liner services operating in the area. Performance benchmarking of the two networks is performed using a set of quantitative Key Performance Indicators (KPIs) and applying a factor-cluster analysis to produce homogeneous clusters of services based on the relevant variables while accounting for sample heterogeneity. Quantitative results mostly confirm the overall better performance of the prospective network and demonstrate that using KPIs and factor-cluster analysis to investigate the performance of maritime networks can provide policymakers with a preliminary wealth of knowledge that can help in setting targeted policy for SSS-oriented initiatives.

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“…Cluster analysis was recently applied to evaluate the competitiveness of Brazilian ports. Maria Rios and de Sousa [40] used cluster analysis to classify 17 Brazilian container terminals into three distinct groups based on competitiveness criteria using a hierarchical cluster analysis. Tovar and Rodríguez-Déniz [41] reviewed the literature on classification methods for port efficiency and applied a frontier-based clustering approach to classify Brazilian ports for efficiency benchmarking.…”

Section: Brazilian Port Performancementioning

confidence: 99%

Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case

Andrade

Lee

et al. 2019

Sustainability

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Data envelopment analysis (DEA) has many advantages for analyzing the efficiency of decision-making units, as well as drawbacks, such as a lack of discrimination power. This study applied bi-objective multiple-criteria data envelopment analysis (BiO-MCDEA), a programming approach used to overcome the limitations of traditional DEA models, to analyze the efficiency of 20 Brazilian ports with a consideration of six input and one output variables from 2010 to 2016. Two time-related variables were included to reflect current problems faced by Brazilian ports experiencing long wait times. The results reveal a significant disparity in port efficiency among Brazilian ports. The top five most efficient ports are those with the highest cargo throughput. A clustering analysis also confirmed a strong correlation between cargo throughput and port efficiency scores. Total time of stay, pier length, and courtyard also had strong correlations with the efficiency scores. The clustering method divided Brazilian ports into three groups: efficient ports, medium efficient ports, and inefficient ports.

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Cluster analysis of the competitiveness of container ports in Brazil

Cited by 43 publications

References 11 publications

A Two-Dimensional Clustering Method for High-Speed Railway Trains in China Based on Train Characteristics and Operational Performance

A Two-Dimensional Clustering Method for High-Speed Railway Trains in China Based on Train Characteristics and Operational Performance

Performance assessment of alternative SSS networks by combining KPIs and factor-cluster analysis

Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case

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