Modeling Demand for Air Cargo in the Colombian Context

DOI

10.2991/amsce-17.2017.31How to use a DOI?

Keywords

cargo; freight modeling; neural networks; linear regression

Abstract

In recent years the growth of air cargo has accelerated, necessitating the assessment of the challenges that this growth will in the future, in order to overcome them and continue contributing to the economic development of the country. Considering the above, this paper proposes several models to estimate demand for air cargo in Colombia, obtained through the use of methodologies such as linear regression and neural networks, which can be used to characterize the current demand and to forecast future demand scenarios given certain contexts to be set. For the estimation of the models presented, information airfreight demand of the top 19 airports in the country was used (in terms of cargo shipped), registered by the Special Administrative Unit of Civil Aeronautics in Colombia (Aerocivil) from 2005 to 2014, besides socioeconomic information on the areas of influence of such airports, obtained mostly by the Administrative Department of National Statistics (DANE) of the same country. Finally a comparison between the results obtained by each modeling methodology, finding better results with neural network models is established.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Miguel Figueroa Loaiza
AU  - Roberto Porto Solano
AU  - Ricardo Simancas
AU  - Victor Higuera Ojito
PY  - 2017/04
DA  - 2017/04
TI  - Modeling Demand for Air Cargo in the Colombian Context
BT  - Proceedings of the 2017 International Conference on Advanced Materials Science and Civil Engineering (AMSCE 2017)
PB  - Atlantis Press
SP  - 132
EP  - 137
SN  - 2352-5401
UR  - https://doi.org/10.2991/amsce-17.2017.31
DO  - 10.2991/amsce-17.2017.31
ID  - FigueroaLoaiza2017/04
ER  -