A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports. (November 2018)

• Record Type: Journal Article
• Authors: Rashed, Yasmine; Meersman, Hilde; Sys, Christa; Van de Voorde, Eddy; Vanelslander, Thierry
• Is Part Of: Transportation research. Volume 117(2018)
• Page Start: 127
• Page End: 141
• Publication Date: 2018-11
• Subjects: Container throughput -- Demand forecasting -- Transport modelling -- Scenarios -- Port capacity -- Hamburg-Le Havre ports; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2018.08.010
• Languages: English
• ISSNs: 0965-8564

Abstract

Highlights: The generic model shows the effect of economic development on container throughput. Different scenarios are provided for container throughput at H-LH and PoA. The study assist stakeholders in the ports infrastructure investment-decision. A relationship exists among EU19 trade indices and conatainer throughput in the H-LH. Elasticity of container throughout in the H-LH to tarde indices is 1.4 on average. Abstract: The decision to provide new or additional capacities in the ports is challenging since it should be supported by a growing demand, which require modelling and forecasting the demand of container throughput. This paper develops an instrument which assists the ports' infrastructure investment-decision-making. A three-step approach is developed by combining the autoregressive distributed lag model with economic scenarios to capture the potential impact of specific risks. The empirical analysis is based on an annual time series (1995–2017) for the total container throughput measured in twenty-foot equivalent units for the main ports within the Hamburg-Le Havre (H-LH) range and a number of economic indices. The study shows that there exists a long-run relationship between the trade indices of EU19 and the total container throughput. The empirical model estimates that the elasticity of the container throughput in the H-LH range to trade indices is about 1.4 on average, and it shows that the moment at which congestion emerges depends strongly on the specific scenario considered.