Regression Based Emission Models for Vehicle Contribution to Climate Change

  • Ander PijoanEmail author
  • Iraia Oribe-Garcia
  • Oihane Kamara-Esteban
  • Konstantinos N. Genikomsakis
  • Cruz E. Borges
  • Ainhoa Alonso-Vicario
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 505)


The reduction of carbon emissions within the transportation sector is one of the most important steps against the threat of global warming. Unless strict emissions-reduction and fuel economy policies are in place, the resulting pollution is expected to increase dramatically along with the amount of vehicles on the roads. An accurate quantification of the emissions produced by each type of vehicle is essential in order to evaluate the social and environmental impacts derived. The literature shows a wide range of pollutant emission models, whether empirical, database centric or regression based. In this paper, we propose and analyze 3 regression based models built on data from pollutant emission databases and knowledge models. The first model is based on an exponential regression that improves the results given in the state of the art. In contrast, the other two models are based on different Artificial Intelligence techniques, namely Artificial Neural Networks and Support Vector Regression, which further improve the results.


Emissions Modeling Environment Traffic Management 



This work was partially supported by: (a) the European Commission through the FP7 Collaborative Project MOVESMART under grant agreement no. 609026. (b) GREEN TRAVELLING (Era NET Transport 6/12/IN/2014/195 label) partially funded by Diputación Foral de Bizkaia through the Plan de Promoción de la Innovación (6/12/-IN/2014/195) and by the Basque Government through the GAITEK program IG-2014/0000133). (c) Industrial Ph.D. grant given by the University of Deusto (2015–2018), and (d) Ph.D. grant PRE_2015_2_003 given by the Basque Government. Finally, the authors would like to thank Dr. Michael André for providing the data of the ARTEMIS driving cycles.


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Ander Pijoan
    • 1
    Email author
  • Iraia Oribe-Garcia
    • 1
  • Oihane Kamara-Esteban
    • 1
  • Konstantinos N. Genikomsakis
    • 1
  • Cruz E. Borges
    • 1
  • Ainhoa Alonso-Vicario
    • 1
  1. 1.Deusto Institute of Technology—DeustoTech EnergyUniversidad de DeustoBilbaoSpain

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