Through this process, infrastructure spending and policy initiatives can be better informed, industrial clusters developed in the correct spatial context, and other bottlenecks and issues can be addressed within the context of the total national freight‐flow landscape.

# **<u>Macro Logistics Planning and Policy applications</u>**

Commodity‐level data enable an understanding of current modal shares, the sustainability of future freight flows given the current trajectories, and where to target modal shift given commodity characteristics.

Zhang et al. (2003) reported on the development of a methodology, based on the CFS data, to systematically estimate state‐wide truck travel demand using the state of Mississippi as a case study. The first step is a detailed commodity flow data analysis including modes and origin‐destinations, which facilitates conversion of road flows into truck trips by using average density of freight and average load per truck to estimate the composition of different truck types carrying different commodity groups. One of the objectives is to identify and prioritize intermodal infrastructure opportunities given that highway networks are experiencing severe congestion ‐ with concomitant externality costs ‐ while other inland transportation modes are underutilized. Feasibility studies for the development of intermodal technologies are enhanced through the availability of commodity‐level data, since specific industries and freight owners can be targeted per route.

Similarly, the State of Alabama used the CFS to develop a state‐wide model that showed the possible impact of new relocated industries and the movement of their goods on the state’s existing transportation infrastructure. Understanding the total character of freight movements along a corridor – its prevalent commodities and potential safety and operational constraints – facilitates the identification of potential options for shifting commodities to alternate modes of transport to alleviate congestion (Anderson and Harris, 2011).

In South Africa, the segmentation enabled by the country’s disaggregated macroscopic FDM informed the development of a domestic intermodal strategy (Havenga et al., 2012) and allowed for confidential input to the national rail policy to change the thrust of rail policy away from one that is focused only on institutional reform and increasing clarity towards one that encourages development and investment (Smith, 2012). The externality cost extension to the transport cost component of South Africa’s logistics cost model is providing ongoing input to national and international discussions on logistics’ contribution to sustainability, as well as providing inputs to carbon taxation (Havenga, 2010 and 2015). Outputs from the South African freight demand model also played a pivotal role in directing large‐scale infrastructure investments, notably the rail and port capital investment programs, with the intent of creating capacity ahead of demand (Havenga, 2012; Transnet, 2015).

The inclusion of imports and exports in spatial disaggregation unlocks analysis of the advantages of supply chain coordination and the elimination of trade barriers for cross‐border and international trade. Reducing delays at South Africa’s two major inland border posts can reduce the costs related to these delays by 55% due to reduced buffer stock required at the destination site, reduced costs of carrying inventory, and reduced vehicle utilization losses (Havenga et al., 2013). Havenga et al. (2016) showed that, for international trade, documentation costs and induced transport costs due to truck and ship standing times are approximately equal to direct port charges. These costs are at least in part avoidable and can be addressed through improved port efficiencies and collaboration between the ports, industry, and the South African Revenue Services.

The inclusion of rural areas in spatial disaggregation allows for detailed analysis which points to opportunities for a revival of rail branch lines in South Africa, which will reduce transport costs and 15