Freight production of agricultural commodities in India using multiple linear regression and generalized additive modelling

Sowjanya Dhulipala and Gopal R. Patil

Transport Policy, 2020, vol. 97, issue C, 245-258

Abstract: Freight transportation has a key role in the economic competitiveness of any nation. India is one of the fastest-growing nations in the world; its agricultural sector plays a vital role in contributing to the country's economy. In this paper, the freight production in the agricultural sector of India is modelled using multiple linear regression (MLR) and generalized additive modelling (GAM) approaches. Using district-level data, factors influencing agricultural freight production are identified and the relationship between them is modelled. The study considered 210 districts of eight states covering South India and parts of Central and Western India. Population, employment in the agricultural sector, gross cropped area, and gross irrigated area are identified as influential factors. GAM approach is a flexible method which can predict non-linear responses for the given predictor variables by allowing non-linear functions for each of them while maintaining additivity. The applicability of GAM in modelling freight production while tackling the non-linear effects of predictor variables on freight production is investigated and encouraging results are achieved. The results revealed that both MLR and GAM models have good modelling efficiency, however, the GAM model outperformed the MLR model in both fitting and predicting. The study can be used in evaluating the commodity movements and transportation demand which in turn will help in decision-making for the provision of freight transportation facilities and policymaking.

Keywords: Freight generation; Agricultural commodities; Linear regression; Generalized additive modelling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.tranpol.2020.06.012

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