2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

An overview of sustainability assessment methodologies

In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

There is considerable federal interest in disaster resilience as a mechanism for mitigating the impacts to local communities, yet the identification of metrics and standards for measuring resilience remain a challenge This paper provides a methodology and a set of indicators for measuring baseline characteristics of communities that foster resilience By establishing baseline conditions, it becomes possible to monitor changes in resilience over time in particular places and to compare one place to another We apply our methodology to counties within the Southeastern United States as a proof of concept The results show that spatial variations in disaster resilience exist and are especially evident in the rural/urban divide, where metropolitan areas have higher levels of resilience than rural counties However, the individual drivers of the disaster resilience (or lack thereof)-social, economic, institutional, infrastructure, and community capacities-vary widely

Disaster Resilience Indicators for Benchmarking Baseline Conditions

This Handbook aims to provide a guide for constructing and using composite indicators for policy makers, academics, the media and other interested parties. While there are several types of composite indicators, this Handbook is concerned with those which compare and rank country performance in areas such as industrial competitiveness, sustainable development, globalisation and innovation. The Handbook aims to contribute to a better understanding of the complexity of composite indicators and to an improvement of the techniques currently used to build them. In particular, it contains a set of technical guidelines that can help constructors of composite indicators to improve the quality of their outputs. It has been prepared jointly by the OECD (the Statistics Directorate and the Directorate for Science, Technology and Industry) and the Applied Statistics and Econometrics Unit of the Joint Research Centre of the European Commission in Ispra, Italy. Primary authors from the JRC are Michela Nardo, Michaela Saisana, Andrea Saltelli and Stefano Tarantola. Primary authors from the OECD are Anders Hoffmann and Enrico Giovannini. Editorial assistance was provided by Candice Stevens, Gunseli Baygan and Karsten Olsen. The research is partly funded by the European Commission, Research Directorate, under the project KEI (Knowledge Economy Indicators), Contract FP6 No. 502529. In the OECD context, the work has benefitted from a grant from the Danish government. The views expressed are those of the authors and should not be regarded as stating an official position of either the European Commission or the OECD.

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Handbook on Constructing Composite Indicators: Methodology and User Guide

The views expressed in this report are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of the following information A great deal of information on the European Union is available on the Internet. It can be accessed through the Europa server

/pdf/tools-for-composite-indicators-building-ohbzk9zc2o.pdf

Tools for Composite Indicators Building

Composite indicators (or indexes) are very common in economic and business statistics for benchmarking the mutual and relative progress of countries in a variety of policy domains such as industrial competitiveness, sustainable development, globalization and innovation. The proliferation of the production of composite indicators by all the major international organizations is a clear symptom of their political importance and operational relevance in policy-making. As a consequence, improvements in the way these indicators are constructed and used seem to be a very important research issue from both the theoretical and operational points of view. This article aims at contributing to the improvement of the overall quality of composite indicators (or indexes) by looking at one of their technical weaknesses, that is, the aggregation convention used for their construction. For this aim, we build upon concepts coming from multi-criteria decision analysis, measurement theory and social choice. We start from the ...

/pdf/non-compensatory-non-linear-composite-indicators-for-ranking-5dzwgalu3k.pdf

Non-Compensatory/Non-Linear Composite Indicators for Ranking Countries: A Defensible Setting

Composite indicators are very common in economic and business statistics for benchmarking the relative progress of countries in a variety of policy domains such as industrial competitiveness, sustainable development, globalisation and innovation. This article provides an analysis of the key uncertainties underlying the mathematical modelling used to construct composite indicators within the broader context of sustainability measurement. A case study of the Environmental Sustainability Index illustrates how these uncertainties can be modelled and estimated.

/pdf/measuring-uncertainties-in-composite-indicators-of-4kspqi0pei.pdf

Michela Nardo

Papers

Handbook on Constructing Composite Indicators: Methodology and User Guide

Tools for Composite Indicators Building

Non-Compensatory/Non-Linear Composite Indicators for Ranking Countries: A Defensible Setting

Measuring Uncertainties in Composite Indicators of Sustainability

A new estimator for Sensitivity analysis of model output: An application to the e-Business Readiness Composite Indicator