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

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  • Javier Rubio-Herrero

    (St. Mary’s University)

  • Jesús Muñuzuri

    (Universidad de Sevilla)

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.

Suggested Citation

  • Javier Rubio-Herrero & Jesús Muñuzuri, 2021. "Indirect estimation of interregional freight flows with a real-valued genetic algorithm," Transportation, Springer, vol. 48(1), pages 257-282, February.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:1:d:10.1007_s11116-019-10050-6
    DOI: 10.1007/s11116-019-10050-6
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    Cited by:

    1. Wenjie Li & Chun Luo & Yiwei He & Yu Wan & Hongbo Du, 2023. "Estimating Inter-Regional Freight Demand in China Based on the Input–Output Model," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    2. Javier Rubio-Herrero & Jesús Muñuzuri, 2023. "Sparse regression for data-driven deterrence functions in gravity models," Annals of Operations Research, Springer, vol. 323(1), pages 153-174, April.

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