2014). This translates into the following four‐step approach for freight demand modeling (De Jong et al., 2004):

- Production and attraction – Estimating the spatial distribution of economic activities, i.e. spatially‐ disaggregated supply (origin) and demand (destination) of commodities and shifts in this spatial distribution over time as the economy responds to policies, macro‐ and microeconomic trends. The output dimension is origin and destination tables in tonnes. In intermediate stages of the production and attraction models, the dimension could be monetary units (trade flows).

- Distribution – Modeling volumetric freight flows between origins and destinations (cells of the OD matrix).

- Modal split – The allocation of the volumetric freight flows to modes (e.g. road, rail and IWT).

- Assignment – The modal flows are converted to vehicle‐units and assigned to networks (ideally, incorporating both truck flows and passenger cars on road networks).<sup>1</sup>

There are essentially three methodologies available for developing freight supply and demand data: a demand‐side approach, a supply‐side approach and a survey approach (Havenga and Pienaar, 2012b).

A demand‐side approach addresses the geographical disparity between supply and demand through flow‐modeling techniques and modality assumptions. It is based on the macroeconomic input‐output (I‐O) model of the economy which describes the interdependencies between industries in terms of intermediate inputs, driven by the developments in final household demand. The country‐level multi‐ sectoral I‐O framework was developed by Leontief (1986) in the 1930s, based on the theory of Keynes who postulated that production is determined by consumption, so‐called market equilibrium, expanded to multiregional or spatial I‐O models by Chenery, Isard and Moses in the 1950s allowing for the calculation of inter‐regional trade (Ivanova, 2014). It requires voluminous disaggregated economic data but renders a deep understanding of freight‐flow patterns and resulting demand for logistics infrastructure. The demand‐side approach therefore has the potential to provide the most comprehensive information but is only possible if adequate data and funding are available (Transportation Research Board, 1997).

A supply‐side approach collects data regarding actual transportation activity on each mode of transportation and constructs a network view of the supply of freight transportation infrastructure. Typically, road freight volumes, due to its fragmented nature, are modeled based on truck counts at weighbridges and counting stations, while actual rail and port data are mostly available through agreements with incumbents. The supply‐side approach is the most cost‐effective option but does not provide detailed O‐D data or commodity visibility.

A survey approach entails the distribution of a questionnaire to a sample of freight logistics stakeholders and, through statistical analysis of the responses, the characteristics of the total freight market are estimated. The strength of the survey approach is that it can elucidate industry dynamics to explain freight flow trends and anomalies. Questionnaire‐based surveys however tend to underperform due to sampling biases and non‐ or partial responses (Kockelman et al., 2009), and present reporting challenges due to confidentiality requirements (Zhang, 2003; Richard Paling Consulting, 2008; Harris and Anderson, 2011). In addition, surveys are often once‐off endeavors with limited repeatability due to time lapses and changes in scope, and fail to provide systemic logistics

> 1 Very few models conclude with converting tonnages flows to vehicle‐units and assigning this to the transport network. Currently this falls outside the scope of the India FDM.

18