ecological and social impacts of freight activity are not accounted for in transport costs (Lewis and Conaty, 2012).

Macroscopic freight demand modeling will be a key enabler for the management of transport and logistics as a macroeconomic production factor within this changing landscape as logistics‐ and connectivity‐related interventions are estimated to have the highest potential to reduce trade costs and to boost global value chain integration (World Bank, 2016d). This thinking is beginning to take root in national logistics policy. For example, in preparing the U.S. _National Freight Strategic Plan,_ understanding of major freight flows was considered a key input for informed planning (US Department of Transportation, 2015).

According to Ivanova (2014), existing empirical literature on freight demand modeling focuses on aggregate trade flows and ignores the differences between various commodities and region pairs in terms of the impact of transport costs on trade patterns and volumes. This aggregated approach does not allow for making policy‐relevant conclusions related to particular industries and commodities and diminishes the usefulness to macro‐econometric analysis. Three decades ago Raza and Aggarwal (1986) understood that aggregate freight‐flow analysis does not reflect the diversities of and the disparities in either the production or consumption processes, nor can it reflect the regional structure of the economy. Tavasszy and De Jong (2014) reiterated that, ideally, freight flow modeling should commence from economic linkages, as freight transport is an outcome of these interactions. De Vries et al. (2012) highlighted that analyses of structural change in developing countries are constrained by the lack of detailed sector data, obscuring a proper assessment of the role of structural transformation in driving aggregate productivity growth through resource allocation to value‐added industries. A more in‐depth understanding of the freight transport market presupposes access to reliable, disaggregated freight‐flow information (Lyk‐Jensen 2011). Disaggregation is required on _inter alia_ commodity flows and geography, and an increased linking of freight modeling with the broader economy, geographically as well as functionally (Tavasszy, 2006).

The relevance of spatial and sectoral disaggregation in macroscopic freight demand modeling is threefold (informed by Ludlow, 2011):

- Macroeconomic planning and policy applications to _inter alia_ assess the health and changing structure of the economy at the national and regional level over time, to conduct sector and regional analysis and to direct spatial planning;

- Macro logistics planning and policy applications to assess transportation demand, engineer an optimal modal balance, identify economic impacts of transportation network investments and support macroeconomic growth through directing capital investments; and

- Mesoscopic applications to target investments and logistics solution development for specific industries or geographies.

These are discussed in turn below using specific examples from the United States and South Africa, the two countries with the longest running time series of freight volume and cost data.

# **<u>Macroeconomic Planning and Policy applications</u>**

The efficient movement of freight within and through a region is vital to its growth and economic development. An understanding of commodity‐level freight flows allows for the assessment of the demand for specific transportation facilities and services (Mesenbourg, 2011) and supports a region’s infrastructure investment decisions (Harris and Anderson, 2011). In the United States, results from the Commodity Flow Survey (CFS) are for example applied to explore the feasibility of dedicated truck lanes on the Interstate 70 corridor connecting Indiana, Illinois, Missouri and Ohio (refer Figure 12). The 13