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Freight generation and geographical effects: modelling freight needs of establishments in developing economies and analyzing their geographical disparities

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  • Prasanta K. Sahu

    (Birla Institute of Technology and Science Pilani)

  • Agnivesh Pani

    (Birla Institute of Technology and Science Pilani)

Abstract

Freight generation (FG) models are important to transportation authorities and planning agencies as they can be used to forecast local/regional/state/national freight movements for facility planning and evaluation of freight-specific investments. Compared to the modelling efforts in passenger transportation, freight transportation remains largely unexplored in developing economies like India mainly because of the absence of national wide commodity flow survey unlike in developed economies. In recognition of this, we collected establishment-based-freight-survey data from 432 establishments in Kerala, India. These data are used to classify the establishments into 13 homogenous industry sectors; several FG models are developed for these sectors. The relationships of annual freight production (FP) and attraction (FA) with establishment size variables (employment and area) were investigated with three modelling approaches. Firstly, a set of 52 practice-ready FG models are estimated using linear regression technique for establishments in each industry sector. Modelling results revealed that employment is a better representative of FP, whereas area represents FA better. The employment-based FP rates in Kerala are found to be lower than that in New York, much like what is observed in passenger transportation; passenger trip rates in developing economies are lower than that in developed countries. Secondly, FG rate tables and Nomograms are developed using Multiple Classification Analysis technique for all industry sectors considering employment and floor area as categories. These nomograms and FG rate tables may be used as planning tools for city developing agencies, while incorporating freight transportation in the overall planning process. Lastly, ANCOVA analyses is provided to assess the geographical disparities on FG and, thereby the model transferability. Study findings will be useful in developing policy guidelines for freight-specific investments, operational strategies, freight movement regulations, and taxation policies, etc. for Indian cities.

Suggested Citation

  • Prasanta K. Sahu & Agnivesh Pani, 2020. "Freight generation and geographical effects: modelling freight needs of establishments in developing economies and analyzing their geographical disparities," Transportation, Springer, vol. 47(6), pages 2873-2902, December.
    • Handle: RePEc:kap:transp:v:47:y:2020:i:6:d:10.1007_s11116-019-09995-5
    DOI: 10.1007/s11116-019-09995-5
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    References listed on IDEAS

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    5. Sahu, Prasanta K. & Qureshi, Danish & Pani, Agnivesh, 2022. "Examining commercial vehicle fleet ownership decisions and the mediating role of freight generation: A structural equation modeling assessment," Transport Policy, Elsevier, vol. 126(C), pages 26-33.
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    7. Leise Kelli de Oliveira & Gracielle Gonçalves Ferreira de Araújo & Bruno Vieira Bertoncini & Carlos David Pedrosa & Francisco Gildemir Ferreira da Silva, 2022. "Modelling Freight Trip Generation Based on Deliveries for Brazilian Municipalities," Sustainability, MDPI, vol. 14(16), pages 1-18, August.

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