IDEAS home Printed from https://ideas.repec.org/p/ude/wpaper/2108.html
   My bibliography  Save this paper

Tasa generadora de viajes para el puerto de Montevideo. Una propuesta metodológica

Author

Listed:
  • Andres Pereyra

    (Departamento de Economía, Facultad de Ciencias Sociales, Universidad de la República)

  • Elías Rubinstein

    (Consultor independiente.)

  • Marcelo Pérez

    (Universidad ORT.)

Abstract

The predictive analysis is becoming increasingly important in the definition of transport policies and in the design of operational activities. In the present article the developed methodology in order to attain an accurate estimation of the movement of containerized cargo on the entrance to the port of Montevideo is presented. Over the last years, the port of Montevideo has gone through an important growth. This, together with its operative features, its placing and interrelation with the country, and its development outlook for the future, requires the use of modern management tools, providing an important opportunity for investigation. The bibliography concerning the modeling of incoming/outgoing containerized cargo in ports is scarce and not always useful. In this article count models were developed, mainly Poisson regression, that explain the production/attraction of containerized cargo trips according to the previous declaration of wharf operation. This Poisson regression included binary and auto regressive lags variables in order to deal adequately with the temporary dimension of the problem.

Suggested Citation

  • Andres Pereyra & Elías Rubinstein & Marcelo Pérez, 2008. "Tasa generadora de viajes para el puerto de Montevideo. Una propuesta metodológica," Documentos de Trabajo (working papers) 2108, Department of Economics - dECON.
  • Handle: RePEc:ude:wpaper:2108
    as

    Download full text from publisher

    File URL: https://hdl.handle.net/20.500.12008/2115
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Trip generation; Poisson regressions; Count regressions; Autoregressive models.;
    All these keywords.

    JEL classification:

    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ude:wpaper:2108. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Andrea Doneschi or the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/derauuy.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.