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

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  • Dhulipala, Sowjanya
  • Patil, Gopal R.

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.

Suggested Citation

  • Dhulipala, Sowjanya & Patil, Gopal R., 2020. "Freight production of agricultural commodities in India using multiple linear regression and generalized additive modelling," Transport Policy, Elsevier, vol. 97(C), pages 245-258.
    • Handle: RePEc:eee:trapol:v:97:y:2020:i:c:p:245-258
    DOI: 10.1016/j.tranpol.2020.06.012
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Sowjanya Dhulipala & Gopal R. Patil, 2021. "Identification of freight generating industry complexes: A descriptive spatial analysis," Growth and Change, Wiley Blackwell, vol. 52(4), pages 2680-2712, December.
    3. Ramirez-Rios, Diana G. & Kalahasthi, Lokesh Kumar & Holguín-Veras, José, 2023. "On-street parking for freight, services, and e-commerce traffic in US cities: A simulation model incorporating demand and duration," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    4. Zhang, Zhaolin & Zhai, Guocong & Xie, Kun & Xiao, Feng, 2022. "Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego," Journal of Transport Geography, Elsevier, vol. 104(C).
    5. Sowjanya Dhulipala & Gopal R. Patil, 2023. "Regional freight generation and spatial interactions in developing regions using secondary data," Transportation, Springer, vol. 50(3), pages 773-810, June.

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