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Spatial and temporal dimensions of container traffic in Nigerian seaports

Ikeogu Christopher C ., Ikeogu Vivian Ngozi, Chukwu Oluchi Ebere

Journal of Sustainable Development of Transport and Logistics

2021

Vol. 6, No. 2

96--108

Generally speaking, cargo demand traffic forecasts are needed tools to rationalize the investment decisions in ports. However, little studies have been carried out in determining the magnitude of container cargo traffic at Nigerian ports, this creates a lacuna that needs to be filled up. The purpose of this study is to ascertain the spatial and temporal dimensions of container traffic in Nigerian seaports from 1990 to 2017. The study uses a survey design and employed descriptive and multivariate statistics to analyze the data. It demonstrates that Nigeria imports lots of commodities in containers than it exports in containers because most of the country’s exports are crude oil and non-containerized goods. Thus, the reason for traffic congestion at the ports and the high cost paid by Nigerian shippers for imported containerized cargo. The study further showed that both imported and exported container traffic had a positive trend in the time series. From the findings, we have shown the need for understanding the magnitude of container traffic at the ports as it helps Ports Authorities in operational decisions regarding port capacity utilization.

Hierarchical Model of Container Ports Throughput

Rozmarynowska M., Smolarek L.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2015

Vol. 9, no. 4

461--466

In this article the attempt has been made to construct hierarchical model of container ports throughput development. The presented hierarchical approach uses the relationships of development of global economy and container flows at different geographical levels: global (container throughput in all seaport on the world), regional (container throughput in the Baltic seaports) and national (container throughput in Polish seaports). Model have been evaluated for their fit and usefulness for predictive purposes.