national data is available, namely districts. The bottom‐up data gathering process facilitates the correlation of the disaggregated data with available aggregates.

# **<u>Model Validation</u>**

- Given the lack of an established I‐O model in India, available data, desktop research and interviews with industry experts and logistics service providers were utilized to construct and refine supply and demand tables.

- Truck counts are a relatively cost‐effective tool to estimate aggregate freight flows and are therefore often used as a validation method for verification of modelled flows (Zhang et al., 2003; Richard Paling Consulting, 2008). In South Africa, for example, a robust national freight flow model (NFFM) was developed that translates the truck counts by the South African National Roads Agency into tonnages (Havenga and Pienaar, 2012a), and the NFFM and South Africa’s spatial freight demand model are used for cross‐validation purposes. Truck flow data for India was used on a case‐ by‐case basis, such as comparing traffic around Varanasi on the Delhi‐Kolkata corridor with the FDM results, and these comparisons were favorable. A next step would be the translation of total truck count data into freight flows for cross‐validation purposes.

- Following project approval of the India FDM based on initial estimates of freight logistics cost reduction opportunities on the Eastern Corridor, twelve months’ subsequent research and fine‐ tuning of the model shows remarkable alignment with initial estimates on an aggregate level. This is in line with experiences for example in Norway where, following numerous model refinements, the overall macro distribution in the new model version was quite similar to that obtained in the former version (Hovi, et al., 2013). However, the refined disaggregated models enable a better description and fit to mesoscopic applications, therefore improving validity and applicability to industry, regional or typology‐level transport challenges, as well as enabling targeted infrastructure investments. This confirms the usefulness of two different levels of freight transport modeling to increase the accuracy of the forecasts and the range of policy applications of the models:

- A fast (low‐resolution) policy analysis model which includes a wide range of factors but limited detail, to aid with initial screening and comparison of policy alternatives (‘first order approximations’); and

- A detailed (high‐resolution) forecasting model which analyzes limited factors in a lot of depth for estimates at a mesoscopic level (e.g. industry, region, typology or network) and to provide inputs for project evaluation.

The reasoning behind this is to address the diverse information needs of the numerous stakeholders, to minimize cost by using the policy‐analysis model for prioritizing options, and to improve accuracy by using the detailed model when long‐term, high‐cost decisions are required (De Jong, Gunn, & Walker, 2004). These principles can be utilized going forward.

# **<u>Research Constraints</u>**

- A national I‐O table for India has been developed as part of the World I‐O database (funded by the European Commission).<sup>3</sup> In the absence of average $/ton values this table cannot be converted to tonnage terms. The project team is endeavoring to develop such values for the I‐O table to serve as a macroscopic validation since the World I‐O database is not regionally disaggregated.

> 3 Refer http://www.wiod.org/new_site/database/niots.htm.

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