The research examines the relationship between the municipality transit demands and the country attributes and builds a generally expendable synthetic model to assess the public transit haul. The data-base effected to support the data processing includes 157 public transit subsidy recipient municipalities located in 4 different Italian regions. The selection of the model independent variables is been marked by the international literature on the topic and it has required the analysis of each variable and a verify of statistical inference by the Fisher and Student tests; we have also assess the risk of collinearity among the selected independent variables by the Variance inflationary factor. The calibration was made by multiple regressions with a growing grade (linear, quadratic, cubic and so on) until remains appeared not-aggregable. After an introduction (section 1) that explains the topic relevance and a state-of-the-art (section 2) aimed also to identify the most important variables affecting the phenomenon, sections 3 and 4 deal with the selection of the significant variables and the model building and calibration respectively; the last section (5) discusses the results and the applicative spin-off.
Un modello sintetico per il dimensionamento dei servizi minimi di trasporto comunale
PETRUCCELLI, Umberto;
2008-01-01
Abstract
The research examines the relationship between the municipality transit demands and the country attributes and builds a generally expendable synthetic model to assess the public transit haul. The data-base effected to support the data processing includes 157 public transit subsidy recipient municipalities located in 4 different Italian regions. The selection of the model independent variables is been marked by the international literature on the topic and it has required the analysis of each variable and a verify of statistical inference by the Fisher and Student tests; we have also assess the risk of collinearity among the selected independent variables by the Variance inflationary factor. The calibration was made by multiple regressions with a growing grade (linear, quadratic, cubic and so on) until remains appeared not-aggregable. After an introduction (section 1) that explains the topic relevance and a state-of-the-art (section 2) aimed also to identify the most important variables affecting the phenomenon, sections 3 and 4 deal with the selection of the significant variables and the model building and calibration respectively; the last section (5) discusses the results and the applicative spin-off.File | Dimensione | Formato | |
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