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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/83810 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/83810 http://eprints.org/relation/ismediumThumbnailVersionOf https://pure.iiasa.ac.at/id/document/83810 83815 2 235530 document 83815 small.jpg image/png 1200c768681728b9192fca934d9f7970 MD5 1840 2018-04-18 06:10:42 https://pure.iiasa.ac.at/id/eprint/15232/6/small.jpg 15232 6 6 image/png other Thumbnails conversion from text to thumbnail_small en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/83810 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/83810 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/83810 archive 353 disk0/00/01/52/32 2018-04-18 06:01:49 2021-08-27 17:30:15 2018-04-18 06:01:49 article show Krisztin T. 2088 0000-0002-9241-8628 pub prog_esm Non-linear spatial autoregressive models; Genetic algorithms; Semi-parametric modeling; Freight generation This paper proposes for the purposes of freight generation a spatial autoregressive model framework, combined with non-linear semi-parametric techniques. We demonstrate the capabilities of the model in a series of Monte Carlo studies. Moreover, evidence is provided for non-linearities in freight generation, through an applied analysis of European NUTS-2 regions. We provide evidence for significant spatial dependence and for significant non-linearities related to employment rates in manufacturing and infrastructure capabilities in regions. The non-linear impacts are the most significant in the agricultural freight generation sector. 2018-06 published Elsevier 10.1016/j.tre.2018.03.003 167 public Transportation Research Part E: Logistics and Transportation Review 114 121-143 TRUE 1366-5545 GLOBIOM FALSE no info:eu-repo/semantics/article