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Forecasting interregional commodity flows using artificial neural networks: an evaluation

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  • H. Murat Celik

Abstract

Previous studies have concluded that the use of artificial neural networks (ANNs) is a promising new technique for modelling freight distribution, supporting the findings of other studies in the area of spatial interaction modelling. However, the forecasting performance of ANNs is still under investigation. This study tests the predictive performance of the ANN Model with respect to a Box--Cox spatial interaction model. It is concluded that the Box--Cox model outperforms ANN in forecasting interregional commodity flows even if ANN had proven calibration superiority in comparison to conventional gravity type models.

Suggested Citation

  • H. Murat Celik, 2004. "Forecasting interregional commodity flows using artificial neural networks: an evaluation," Transportation Planning and Technology, Taylor & Francis Journals, vol. 27(6), pages 449-467, September.
  • Handle: RePEc:taf:transp:v:27:y:2004:i:6:p:449-467
    DOI: 10.1080/0308106042000293499
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    Cited by:

    1. Michael L. Lahr & João Pedro Ferreira & Johannes R. Többen, 2020. "Intraregional trade shares for goods‐producing industries: RPC estimates using EU data," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1583-1605, December.
    2. Yang, Yi, 2018. "Improving estimates of subnational commodity flows in LCA for policy support: A US case study," Energy Policy, Elsevier, vol. 118(C), pages 312-316.

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