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Sökning: WFRF:(Baak Jaap)

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1.
  • Abate, Megersa, 1980-, et al. (författare)
  • A disaggregate stochastic freight transport model for Sweden
  • 2019
  • Ingår i: Transportation. - : Springer. - 0049-4488 .- 1572-9435. ; 46:3, s. 671-696
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents estimation results for models of transport chain and shipment size choice, as well as an implementation of the estimated disaggregate models (for two commodity groups), in the context of the national freight transport model for Sweden. The new model is a disaggregate and stochastic (logit) model, whereas the existing Swedish national model is deterministic. One advantage of the new approach is that it bases the underlying behavior of shippers on a stronger empirical foundation (that is micro-data from the Swedish Commodity Flow Survey, CFS). Another advantage is that it overcomes a well-known disadvantage of deterministic models that lead to implausibly large responses to changes in scenario or policy variables. Although estimation and implementation of aggregate stochastic models were done before, in the context of a national freight transport forecasting model, we think this is the first implementation of disaggregate freight transport chain and shipment size models estimated on choice data for individual shipments, certainly in Europe. We carried out a number of model runs with both versions of the implemented model to compare elasticities and found that transport cost and time elasticities for tonne-km are smaller (in absolute values) in the disaggregate stochastic model than in their deterministic counterparts.
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2.
  • Wilkinson, Mark D., et al. (författare)
  • Comment : The FAIR Guiding Principles for scientific data management and stewardship
  • 2016
  • Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 3
  • Tidskriftsartikel (refereegranskat)abstract
    • There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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