Modeling of container throughput in the European port system-analysis of container liner shipping networks
(English) This thesis records the evolution of the container throughput dynamics of the European port system from 2001-2020, analyses the dynamics of the European container port hierarchy with methodologies that have not been used in that context, applies network analysis on the liner shipping netwo...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/690198 |
| Acceso en línea: | http://hdl.handle.net/10803/690198 https://dx.doi.org/10.5821/dissertation-2117-403631 |
| Access Level: | acceso abierto |
| Palabra clave: | Container ports Port hierarchy Liner shipping networks Container throughput dynamics Point forecasts Prediction intervals Àrees temàtiques de la UPC::Enginyeria civil 627 |
| Sumario: | (English) This thesis records the evolution of the container throughput dynamics of the European port system from 2001-2020, analyses the dynamics of the European container port hierarchy with methodologies that have not been used in that context, applies network analysis on the liner shipping networks of individual shipping companies and proposes a model for short-term forecasts of container throughput and their accompanying prediction intervals. Chapter 2 explores the dynamics of container throughput in the European port system from 2001-2020 at the level both of groups of ports and individual ports (Notteboom, 2010). The evolution of the container throughput is examined by the application of concentration indicators. The market share of port groups or ports in the total throughput of the system, the normalized Herfindahl–Hirschman Index and a customized form of shift-share analysis are used for this purpose. The influence of the two crises that occurred during the examined period, the global financial crisis of 2009 and the health crisis of 2020, on the container throughput dynamics is explored. The dynamics of the European container port hierarchy are analyzed in chapter 3 by the application of 3 methodologies. First, two types of rank-size models are used in order to investigate the formation of the hierarchies and the dynamics of container throughput distribution. Second, a methodology based on the principals of Analytic Hierarchy Process (AHP) combined with an allometric growth model is introduced in order to explore the relative growth of large ports as a group upon groups of medium-sized and small ports. Third, a Markov chain modeling is applied in order to investigate the mobility of ports within the hierarchy of the European container port system. The dataset that is used in all the methodologies consists of the annual container port throughputs in TEUs of the top-100 European container ports for every year between 2001 and 2020. Chapter 4 analyzes the container liner shipping networks of the liner operators of Maersk and COSCO Shipping in 2001, 2007 and 2010. First, the whole network of the carriers is analyzed through global network indicators and visualization of the networks. Second, the shipping networks are investigated for the presence of complex network properties by fitting a power law distribution to the degree distribution of the networks. Third, the positions of the ports in the companies’ networks are explored by calculating their degree and betweenness centrality. Fourth, the weighted degree of the ports and the strongest inter-port links are presented using the frequency (weekly calls) as weight of the links. Finally, the chapter focuses on the regional networks of the companies and their evolution. A combined SARIMA-(G) ARCH model is proposed in chapter 5 in order to produce short-term forecasts of container throughput and prediction intervals of these forecasts. The uncertainty of the forecasts is expressed by the prediction intervals associated with the point forecasts. The point forecasts are generated form the SARIMA part of the model and the prediction intervals from the ARCH or GARCH part. ARCH/GARCH models are used in order to model and forecast the volatility of container throughput time series. The combined SARIMA-(G)ARCH model is applied to monthly container throughput data of the port of Barcelona in order to produce short-term point forecasts and its accompanying prediction intervals. |
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