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Link to original content: https://doi.org/10.1007/978-3-031-37114-1_41
A Machine Learning Method for the Analysis of Urban Italian Mobility | SpringerLink
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A Machine Learning Method for the Analysis of Urban Italian Mobility

  • Conference paper
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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14107))

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Abstract

The aim of this paper is to consider a method of machine learning to analyze the problem of the sustainability of the urban transport in Italian cities. First of all we recall the definition of sustainable mobility then we present some indicators considered in our analysis.

The methodology used in this paper are decisional trees.

We both consider classification and regression trees. We have chosen two different dependent variables one for classification trees (a categorical variable: Macroregion according to NUTS 1: North West, North East, Centre, Islands and South of Italy) and one for regression trees (a quantitative variable: PM10 maximum number of days in excess of the human health protection limit foreseen for PM10). In order to test the performance of this methodology we have applied random forest.

The analysis has been performed using SAS language.

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Notes

  1. 1.

    These cities have not be considered in this analysis: Andria e Trani.

  2. 2.

    This is the procedure used by SAS language for decision trees.

  3. 3.

    Missing values are treated using the option of HPSLIT assignmissing = similar.

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Author information

Authors and Affiliations

  1. DEAMS – Department of Economic, Business, Mathematic and Statistical Sciences “Bruno de Finetti”, University of Trieste, Valerio, 4/1 – 34127, Trieste, Italy

    Gabriella Schoier & Giuseppe Borruso

Authors
  1. Gabriella Schoier
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  2. Giuseppe Borruso
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Corresponding author

Correspondence to Gabriella Schoier .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Schoier, G., Borruso, G. (2023). A Machine Learning Method for the Analysis of Urban Italian Mobility. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14107. Springer, Cham. https://doi.org/10.1007/978-3-031-37114-1_41

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  • DOI: https://doi.org/10.1007/978-3-031-37114-1_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37113-4

  • Online ISBN: 978-3-031-37114-1

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