Abstract
In a time of economic austerity, more pressure is being put on the existing transport systems to be more sustainable and, at the same time, more equitable and socially inclusive. Regular public road transportation traditionally uses fixed routes and schedules, which can be extremely expensive in rural areas and certain periods of the day in urban areas due to low and unpredictable demand. Demand Responsive Transportation systems are a kind of hybrid transportation approach between a taxi and a bus that try to address these problems with routes and frequencies that may vary according to the actual observed demand. Demand Responsive Transportation seems to have potential to answer the sustainability and social inclusion challenges in a context of austerity. However, DRT projects may fail: it is not only important to solve the underlying model in an efficient way, but also to understand how different ways of operating the service affect customers and operators. To help design DRT services, we developed an innovative approach integrating simulation and optimization. Using this simulator, we compared a real night-time bus service in the city of Porto, Portugal, with a hypothetical flexible DRT service for the same scenario.

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
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.

Abstract
Urban mobility services generate massive amounts of raw data that are usually not explored in depth by the entities that own them. Visualisation techniques could improve knowledge extraction and decision-making, as well as support the reengineering of those services. Some studies in Information Visualisation provide a domain-independent classification for visualisations based on their own characteristics and the data they support, although independent of their context of use. We propose a classification for visualisations of urban mobility data according to their context of use and their characteristics. Our first results are encouraging and are supported by a user-centred design process carried with urban mobility experts, in which we developed and evaluated a set of visualisation prototypes. The conclusions form a first effort towards a conceptual framework proposal for classifying visualisations of this domain, and are expected to guide researchers and practitioners searching for adequate ways to visually represent their data.

Abstract
The adoption of smart cards in urban public transport has fundamentally changed how transport providers manage and plan their networks. Traveller information services, in particular, have leveraged this contextual data for targeting passengers and providing relevant information. Thus, it becomes increasingly relevant for the next generation of services to obtain on-time contextual passenger information, to support the development of intelligent information services. In this paper an adaptation of the Top-K algorithm is proposed for predicting journey destination, applied to different scenarios in public transport. The performance and efficiency are analysed and compared to a decision tree classifier. Finally, the feasibility and potential of applying the proposed methods to large-scale systems in a real-world environment is discussed.

Abstract
This study develops a tool to assess livability in European cities covering two components of livability: human wellbeing and environmental impact. First, we propose a conceptual model to assess cities' livability, that extends the concept of urban livability to include a component related to environmental sustainability. Second, we address the measurement of cities' livability. For this purpose, a new composite indicator was constructed based on a data envelopment analysis model specified using a directional distance function. In addition to assigning a summary measure of performance for each city, the composite indicator can be used to guide improvements concerning different livability objectives. One of the innovative features of the model proposed is to enable, by the specification of different directional vectors, focusing separately on each component of livability (e.g., human wellbeing and environmental impact) or alternatively considering cities' potential for improvement in all indicators simultaneously. In addition, it is possible to incorporate a new type of weight restrictions, specified as assurance regions type I, that reflect the relative importance of both desirable and undesirable outputs in percentage terms. Finally, this paper approached the assessment of the evolution of cities' performance over time using the Luenberger productivity indicator.