Indirect estimation of interregional freight flows with a real-valued genetic algorithm

Abstract

This paper introduces a method for estimating the interregional transportation of certain commodities in those cases where the commodity flows are not readily available and only aggregated flows per origin-destination pair are provided. We use a doubly-constrained gravity model to find a matrix of aggregated flows that is as similar as possible to the available data, in the sense of the standardized root mean square error. This model is calibrated via a real-valued genetic algorithm that uses a combination of global and local searches to find a set of optimal parameters of the deterrence function under study in the gravity model. This method is introduced as an application to estimating the disaggregated flows of ten different products among the fifteen regions of peninsular Spain between 2007 and 2016. After testing several formulations, we conclude that an exponential deterrence function calibrated with data from 2010 is as effective to estimate the flows in this 10-year span as other more complex options, which emphasizes the time transferability of our model.

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Contributions

Javier Rubio-Herrero: Literature search and review, manuscript writing, statistical tests, and optimization methods. Jesús Muñuzuri: Literature search and review, manuscript writing.

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Correspondence to Javier Rubio-Herrero.

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Rubio-Herrero, J., Muñuzuri, J. Indirect estimation of interregional freight flows with a real-valued genetic algorithm. Transportation 48, 257–282 (2021). https://doi.org/10.1007/s11116-019-10050-6

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Keywords

  • Transport modeling
  • Metaheuristics
  • Logistics
  • Spatial analysis