Regional freight generation and spatial interactions in developing regions using secondary data

In this paper, regional freight generation models are developed for twenty industry sectors in a region comprising 101 districts of India using secondary data. We provide insights into investigating spatial dependencies, identifying the type of spatial interactions present, and adopting appropriate...

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Bibliographic Details
Published in:Transportation (Dordrecht) 2023-06, Vol.50 (3), p.773-810
Main Authors: Dhulipala, Sowjanya, Patil, Gopal R.
Format: Article
Language:English
Subjects:
Autocorrelation
Commodities
Data collection
Developing countries
Economic development
Economic Geography
Economics
Economics and Finance
Employment
Endogenous
Engineering Economics
GDP
Gross Domestic Product
Industrial development
Infrastructure
Innovation/Technology Management
LDCs
Literature reviews
Logistics
Marketing
Modelling
Organization
Planning
Regional development
Regional/Spatial Science
Regions
Regression analysis
Regression models
Spatial analysis
Spatial dependencies
Spillover effect
Transportation
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Summary:In this paper, regional freight generation models are developed for twenty industry sectors in a region comprising 101 districts of India using secondary data. We provide insights into investigating spatial dependencies, identifying the type of spatial interactions present, and adopting appropriate spatial models. Non-spatial and spatial regression models are developed, addressing different spatial interactions. The roles of economic, locational, geographical, and transportation infrastructure variables in estimating freight production and addressing spatial autocorrelation are explored. Sectoral employment, an economic variable, is found significant in estimating freight production of 20 sectors considered. Locational, geographical, and transportation infrastructure variables helped to address spatial autocorrelation for some sectors. Spatial analysis showed correlated effects and endogenous spatial interactions with global spillover feedback, suggesting that spatial error and spatial lag models are appropriate. The study framework can help researchers and planners in correcting for spatial autocorrelation while modelling freight generation. The study forms a paradigm of freight generation modelling with the available secondary data in the absence of comprehensive freight databases and systematic modelling frameworks.
ISSN:0049-4488
1572-9435
DOI:10.1007/s11116-021-10261-w