This paper proposes a self-contained reference for both policy makers and scholars who want to address the problem of efficiency and effectiveness of local public transport (LPT), with special emphasis on urban transit, in a sound empirical way. Framing economic efficiency studies into a transport planning perspective, it offers a critical discussion of the existing empirical studies, relating them to the main methodological approaches used. The connection between such perspectives and Operations Research studies dealing with scheduling and tactical design of public transport services is also developed. The comprehensive classification of selected relevant dimensions of the empirical literature, namely inputs, outputs, kind of data analysed, methods adopted and policy relevant questions addressed, and the systematic investigation of their interrelationships allows us to summarise the existing literature and to propose desirable developments and extensions for future studies in the field

This paper proposes an Ontology-Based Data Management (OBDM) approach to coordinate, integrate and maintain the data needed for Science, Technology and Innovation (STI) policy development. The OBDM approach is a form of integration of information in which the global schema of data is substituted by the conceptual model of the domain, formally specified through an ontology. Implemented in Sapientia, the ontology of multi-dimensional research assessment, it offers a transparent platform as the base for the assessment process; it enables one to define and specify in an unambiguous way the indicators on which the evaluation is based, and to track their evolution over time; also it allows to the analysis of the effects of the actual use of the indicators on the behavior of scholars, and spot opportunistic behaviors; and it provides a monitoring system to track over time the changes in the established evaluation criteria and their consequences for the research system. It is argued that easier access to and a more transparent view of scientific-scholarly outcomes help to improve the understanding of basic science and the communication of research outcomes to the wider public. An OBDM approach could successfully contribute to solve some of the key issues in the integration of heterogeneous data for STI policies

We illustrate the usefulness of an Ontology Based Data Management (OBDM) approach to develop an open information system, allowing for a deep level of interoperability among different databases, and accounting for additional dimensions of data quality compared to the standard dimensions of the OECD (2011) Quality Framework. Recent advances in engineering in computer science provide promising tools to solve some of the crucial issues in data integration for Research and Innovation (R&I).

Probabilistic Discrete Choice Models (PDCM) have been extensively used to interpret the behavior of heterogeneous decision makers that face discrete alternatives. The classification approach of Logical Analysis of Data (LAD) uses discrete optimization to generate patterns, which are logic formulas characterizing the different classes. Patterns can be seen as rules explaining the phenomenon under analysis. In this work we discuss how LAD can be used as the first phase of the specification of PDCM. Since in this task the number of patterns generated may be extremely large, and many of them may be nearly equivalent, additional processing is necessary to obtain practically meaningful information. Hence, we propose computationally viable techniques to obtain small sets of patterns that constitute meaningful representations of the phenomenon and allow to discover significant associations between subsets of explanatory variables and the output. We consider the complex socioeconomic problem of the analysis of the utilization of the Internet in Italy, using real data gathered by the Italian National Institute of Statistics.

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