making, alleviates public safety concerns by providing data for conducting risk analyses and security assessments, and identifies emergency response and preparedness requirements (Duych, 2011).

The relevance of spatial and sectoral flows in macroscopic freight demand analysis is clear. In the next section, an overview of the development of freight demand models is provided to inform the methodology to develop India’s FDM.

# **Background to Macroscopic Freight Demand Modeling**

Transportation demand modeling was formalized within urban passenger transportation planning during the 1960s with the development of the sequential four‐step model. The four steps refer to trip generation, trip distribution, mode choice and route assignment (De Jong et al., 2004).

Walter et al. (1998) expanded this approach and include freight modeling in their approach based on the premise that passenger flow models rely on socioeconomic factors rather than profit maximization which is often the case with freight which requires a mathematical optimizing algorithm. They highlight the problems associated with the absence of intercity data and propose and develop an approach to develop an investment analysis model that integrates regional input‐output models, spatial interaction models, cost‐benefit analysis and geographic information systems. Ortuzer and Willumsen (2011) admit that a large part of their seminal work is around passengers with a strong urban emphasis even though freight movement, and especially road haulage, is an important source of congestion, noise and other traffic problems. They maintain that policy options are limited, but that freight demand modeling can play an important role in developing countries where exports and market access for underdeveloped areas are important.

Specific characteristics of the freight transport industry therefore render freight transport demand modeling more challenging from a methodological point of view than passenger transport demand modeling. These include (De Jong et al., 2004; Müller et al., 2012; Ivanova, 2014):

- The heterogeneity of freight (from parcel deliveries with many stops to single bulk shipments of hundreds of thousands of tonnes) and the heterogeneity of geographies;

- The diversity of stakeholders (freight owners, customers, freight forwarders, logistics service providers, government and communities) and components (technology, infrastructure, fixed‐ and moving assets) within freight supply chains requiring coordination and integration. These lead to complex relationships and, frequently, conflicting requirements.

This is compounded by the mega scale and extended lifespan of freight transportation infrastructure such as railways and ports (Rodrigue, 2016). In addition, specific modeling requirements impact the freight modeling process. According to Müller et al., 2012, the requirements include:

- Modeling and tracking the link between freight transport and final consumption (often proxied by GDP growth);

- Accurate conversion of trade values to trade volumes (the majority of economic data is published in monetary terms);

- The choice of vehicle type as impacted by cost sensitivities and freight requirements; and

- Ability to link modal interaction, mainly rail, road and waterways, as well as the interaction with passenger capacity requirements.

The complexity induced by these characteristics and requirements is the main reason why the development of freight transport modeling has been lagging the development of passenger transport modeling. The basic premise is however straightforward: The spatial patterns of production and consumption generate freight transportation flows to meet the needs of end consumers (Ivanova, 17