Uncertainties in future global trade flows due to changes in trade agreements, transport technologies or sustainability policies, will affect the patterns of global freight transport and, as a consequence, also affect the demand for major freight transport infrastructures such as ports and hinterland networks. Policy makers face the challenge of making robust policies and investments that sustain and promote economic development amidst the various uncertainties. This thesis proposes a set of empirically grounded quantitative models of global freight transport that can support strategic decision making about investments in freight transport infrastructures. We specify, estimate and validate these models for both maritime and hinterland transport, and apply them in comprehensive analyses of the EU’s and the global container transport networks.@en

The global container transport system is changing quickly. Ports can be severely affected by these changes; therefore, ports need insight into how the system might change and what the impact of this will be on their competitive position. Given the intrinsic complexity of the container transport system and the presence of a wide range of deeply uncertain factors affecting the system, we use an exploratory modeling approach to study future scenarios for the global container network. Using scenario discovery and worst-case discovery, we assess the implications of various uncertain factors on the competitive position of the port of Rotterdam. It is found that overall the competitive position of Rotterdam is quite robust with respect to the various uncertain factors. The main vulnerability is the quality of the hinterland connections. A modest deterioration of the quality of the hinterland connections, resulting in increased travel time, will result in a loss of throughput for Rotterdam.

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This paper presents a strategic model for port-hinterland freight distribution networks. The approach utilizes a combination of a multi-objective optimization model to estimate locations and networks of distribution centers and an assignment model that recognizes distributed service level preferences. Our example application concerns the European continent and is transferable to other regions. The model calibration is able to explain the European port-hinterland distribution structures satisfactorily. We compute novel performance measures that take into account port-hinterland distribution structures. The measures include port-hinterland transport cost, port-hinterland transport time, and distribution center-hinterland transport time. These measures can provide inputs for port-connectivity studies.@en

The objective of this chapter is to discuss methods and techniques for a quantitative and descriptive analysis of future container transport demand at a global level. Information on future container transport flows is useful for various purposes. It is instrumental for the assessment of returns of investments in network infrastructure and fleets, the prediction of environmental impacts of transport and the analysis of success of governmental policies about maritime markets and hinterland transport systems. As the future development of global freight flows is unknown and quite uncertain, models are used to define plausible and consistent scenarios of the future performance of the sector. Models of global container transport demand can follow the generic architecture available for freight transport modelling. We describe the methods and techniques available by reviewing the literature with a specific focus on global level freight modelling and treat the subject in two main parts. One part involves modelling the demand for movement between regions, i.e. the outcome of the processes of production, consumption and trade. The second part involves the modelling of demand for transport services by mode and route of transport, including the demand for maritime and inland port services. In both parts we find that surprisingly little research has been conducted specifically for descriptive models of global container movements. Future work can focus on the linkages between container transport and supply chain management. This may include a better understanding of the contribution of shippers’ preferences to observed shipping choices. Also, future developments in geographic restructuring of supply chains because of changes in manufacturing locations or distribution structures, could be looked into. Finally, as global, integrative models do not yet exist, combining new trade and transport network models in a consistent way should provide new tools for long term forecasting and policy analysis.

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