The contribution of this paper consists in applying several techniques to estimate a composite indicator of sustainable urban mobility for 116 Italian provincial towns, as proposed by Luzzati and Gucciardi (2015). The aim is to provide the decision-maker with a richer set of information on sustainable mobility, making explicit the impact of the techniques used on the values obtained. A set of 16 indicators with a nested structure has been selected and 33 combinations of normalization, weighting, and aggregation techniques are applied. The results are presented by city, by city groupings (size and location), and focusing on the ten largest Italian cities. It is found that small cities perform better in terms of sustainable mobility than medium or large cities. As expected, in some cases (e.g., Milan) the intervals of the results obtained are quite large, indicating that the techniques used to normalize, weight, and aggregate the indicators highly affect the performance indexes. In other cases (e.g., Rome) the results are stable across methodologies.

Composite indicators of sustainable urban mobility: Estimating the rankings frequency distribution combining multiple methodologies

Danielis, Romeo
;
Rotaris, Lucia;Monte, Adriana
2018-01-01

Abstract

The contribution of this paper consists in applying several techniques to estimate a composite indicator of sustainable urban mobility for 116 Italian provincial towns, as proposed by Luzzati and Gucciardi (2015). The aim is to provide the decision-maker with a richer set of information on sustainable mobility, making explicit the impact of the techniques used on the values obtained. A set of 16 indicators with a nested structure has been selected and 33 combinations of normalization, weighting, and aggregation techniques are applied. The results are presented by city, by city groupings (size and location), and focusing on the ten largest Italian cities. It is found that small cities perform better in terms of sustainable mobility than medium or large cities. As expected, in some cases (e.g., Milan) the intervals of the results obtained are quite large, indicating that the techniques used to normalize, weight, and aggregate the indicators highly affect the performance indexes. In other cases (e.g., Rome) the results are stable across methodologies.
19-ott-2017
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https://www.tandfonline.com/doi/full/10.1080/15568318.2017.1377789
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2914969
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