Abstract
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.
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Appendices
Appendix 1
See Fig.
Spatial correlograms of a NIC 11 b NIC 16
4.
Figure 4a, b shows the spatial correlograms for NIC 11 and NIC 16. The correlograms depict how the spatial autocorrelation changes with distance. The intersection between the correlogram and the dashed zero axis determines the range of spatial autocorrelation. For NIC 11, this occurs at 154.7 km, which is in the range (150, 225 km). Beyond this range, the autocorrelation is first negative and then fluctuates around the zero line. For NIC 16, the autocorrelation is first zero at 167.6 km, in the range (150, 225 km). The histogram at the bottom of the graph shows the number of pairs of observations in each bin. In the present analysis, each bin has more than sufficient observation pairs. The first bin, which seems small, used 64 pairs for the computation.
Appendix 2
See Table
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10.
Appendix 3
See Fig.
Observed versus predicted Ln(GVO) plots for validation
5.
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Dhulipala, S., Patil, G.R. Regional freight generation and spatial interactions in developing regions using secondary data. Transportation 50, 773–810 (2023). https://doi.org/10.1007/s11116-021-10261-w
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DOI: https://doi.org/10.1007/s11116-021-10261-w