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After the terrorist attack on the World Trade Center in New York on 11 September 2001 there is an increased fear of terrorism. The transport sector seems to be a main target for terrorism. Not only air‐traffic, but also main traffic junctions such as tunnels and bridges may be possible targets of terrorists. The horrific attacks on several trains in Madrid in 2004, illustrate ones more the credibility of these threats. This paper addresses the question how to measure the indirect effects of a terrorist attack on transport infrastructure via an increase in transport costs, and discusses a approach how a government may find the economically most vulnerable links in the infrastructure network. It is proposed to use a spatial applied general equilibrium model in the new economic geography tradition to measure the indirect economic effects.

Modelling has been a mainstay of conventional planning support tools (PSTs) since the 1960s and is instrumental in transport and land use planning decision-making. Numerous studies have been conducted to model the potential impacts of emerging vehicle automation and sharing technologies. A systematic review of recent modelling studies of autonomous and shared vehicles in the research literature examines the extent of their contribution to ‘smart’ mobility knowledge. The findings suggest a limited knowledge base from which to support future planning. PSTs that can offer more pluralistic, discursive, and transparent methods in order to understand and proactively shape a transition to a planned urban future are also needed.

The purpose of this paper is to present the results of South Africa’s national freight demand model and related logistics cost models, and to illustrate the application of the modelling outputs to inform macrologistics policy.

Spatially and sectorally disaggregated supply and demand data are developed using the input-output (I-O) model of the economy as a platform, augmented by actual data. Supply and demand interaction is translated into freight flows via a gravity model. The logistics costs model is a bottom-up aggregation of logistics-related costs for these freight flows.

South Africa’s logistics costs are higher than in developed countries. Road freight volumes constitute 80 per cent of long-distance corridor freight, while road transport contributes more than 80 per cent to the country’s transport costs. These challenges raise concerns regarding the competitiveness of international trade, as well as the impact of transport externalities. The case studies highlight that domestic logistics costs are the biggest cost contributor to international trade logistics costs and can be reduced through inter alia modal shift. Modal shift can be induced through the internalisation of freight externality costs. Results show that externality cost internalisation can eradicate the societal cost of freight transport in South Africa without increasing macroeconomic freight costs.

Systematic spatially disaggregated commodity-level data are limited. There is however a wealth of supply, demand and freight flow information collected by the public and private sector. Initiatives to create an appreciation of the intrinsic value of such information and to leverage data sources will improve freight demand modelling in emerging economies.

A spatially and sectorally disaggregated national freight demand model, and related logistics costs models, utilising actual and modelled data, balanced via the national I-O model, provides opportunities for increased accuracy of outputs and diverse application possibilities.