Abstract
This paper proposes an ontology-based approach to support the process of visualizing urban mobility data. The approach consists of building a visualization-oriented urban mobility ontology, focused on themes such as ridership, vehicle flows and the like. Existing ontologies focus on modelling the overall structure of transportation networks, and do not address the formalization of such themes. The ontology also allows characterizing visualization techniques with human perception factors, so that they can be used to automatically infer recommended techniques for a dataset. The ultimate goal is to benefit decision makers, by providing an ontology that can assist with the process of developing semantically-rich visualizations, with increased data interoperability and knowledge extraction capabilities. We provide an example with real data of the public transportation system of the city of Porto, Portugal. The example shows the semantic characterization of a visualization technique, and how semantics can assist the task of automatically recommending visualizations. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Mobile devices are increasingly pervasive and are transforming information distribution paradigms. A rapidly growing segment of urban public transport passengers carry mobile computing devices, permanently and on the move. In a context of thinning financial resources, getting customers involved in the actual delivery of a public transport service as real-time information consumers and providers, may be a powerful method to enhance travel experience while reducing operational costs for the service operator. Each and every customer travelling on a public transport network has unique knowledge about the service operation as it unfolds. This paper proposes a framework that aims to unify public transport passengers' collective intelligence through crowdsourcing, using their mobile computing devices and dedicated web services. It strives to intensify win-win relationships between public transport passengers and operators. The structured exchange of information is sustained by a validation mechanism for data reliability, and an incentive mechanism to encourage passenger participation. Passengers benefit from rich real-time data tailored to their profiles, to ease their journeys and improve travel experience, in exchange for their own participation providing and validating information. Operators gain access to rich customer generated data, which in an aggregated format may provide a real-time assessment of customer experience and of local performance across the entire network operation. Operators may be required to reward travellers who become prolific co-creators of the public transport service, but higher customer experience levels, lesser needs for investment in controlling mechanisms, and continuous free monitoring of customer opinions can jointly lead to financial returns. (C) 2013 The Authors. Published by Elsevier Ltd.

Abstract
In Public Transportation systems, accurate representation of information has been key for users to take the more advantages of the services and fulfil their travel needs. The Spider Map is a particular schematic representation to illustrate all travel possibilities from a given geographical location. Recently the Spider Map interaction and generation process automation have been studied, although there are yet different possibilities to explore in the technological approach and interactive solution fields. This research proposes the Cobweb solution, dissecting the components of an Interactive Spider Map, focusing on the interactive dynamic potential, the ways to represent information, challenging the existing standard. An implementation of this solution is evaluated using carefully designed user tests to validate the design decisions, taking into consideration different interaction alternatives for each phase of the map generation. The results show improvements regarding the traditional alternative, with positive user response, valuing highly context awareness and information features.

Abstract
In the last decade, public transportation providers have focused on improving infrastructure efficiency as well as providing travellers with relevant information. Ubiquitous environments have enabled traveller information systems to collect detailed transport data and provide information. In this context, journey prediction becomes a pivotal component to anticipate and deliver relevant information to travellers. Thus, in this work, to achieve this goal, three steps were defined: (i) firstly, data from smart cards were collected from the public transport network in Porto, Portugal; (ii) secondly, four different traveller groups were defined, considering their travel patterns; (iii) finally, decision trees (J48), Naive Bayes (NB), and the Top-K algorithm (Top-K) were applied. The results show that the methods perform similarly overall, but are better suited for certain scenarios. Journey prediction varies according to several factors, including the level of past data, day of the week and mobility spatiotemporal patterns.

Abstract
Available visual temporal querying tools do not provide the means for formulating complex temporal queries. For these queries users have to adopt text-based querying languages, such as SQL. The problem, however, is that using text-based languages is less comfortable than using visual tools and, most important, in some cases temporal queries can be extremely difficult to formulate for users that do not possess programming competences. In this paper we propose the Time Automaton, a highly flexible visual mechanism that enables the formulation of a large set of different types of temporal queries, ranging from the simple to the most complex ones. To prove its practical application we created a tool that implements the mechanism and used it to analyze a real dataset. Time Automaton was validated against a representative sample of temporal queries extracted from the matured OWL-Time Ontology. In order to understand if users, with or without programming competences, could understand and use the Time Automaton we conducted a usability experiment.

Abstract
This paper discusses different models that can be used to construct composite indicators with both desirable and undesirable output indicators. Two approaches are considered. The first is an indirect approach, based on a traditional Data Envelopment Analysis model, requiring a prior transformation in the measurement scale of the undesirable outputs. The second is a direct approach, based on a directional distance function model. The use of a directional distance function allows for the accommodation of undesirable indicators in their original form. The main limitations of these approaches are discussed related to the data transformation in the case of the indirect approach and the possibility to obtain negative margin rates of substitution between the desirable and undesirable outputs in the case of the direct approach. These issues lead to the proposal of a new composite indicator model based on a directional distance function that overcomes the limitations associated with the existing approaches. The incorporation of information on the relative importance of individual indicators using weight restrictions is discussed. Proposed here is an enhanced formulation of weight restrictions, in the form of assurance regions type I, that reflects the relative importance of the indicators in percentage terms. The models are illustrated in the assessment of Brazilian hydropower plants and are suitable for any assessment involving the aggregation of key performance indicators whenever undesirable outputs are present.