Abstract
A methodology for estimating the destination of passenger journeys from automated fare collection (AFC) system data is described. It proposes new spatial validation features to increase the accuracy of destination inference results and to verify key assumptions present in previous origin-destination estimation literature. The methodology applies to entry-only system configurations combined with distance-based fare structures, and it aims to enhance raw AFC system data with the destination of individual journeys. This paper describes an algorithm developed to implement the methodology and the results from its application to bus service data from Porto. The data relate to an AFC system integrated with an automatic vehicle location system that records a transaction for each passenger boarding a bus, containing attributes regarding the route, the vehicle, and the travel card used, along with the time and the location where the journey began. Some of these are recorded for the purpose of allowing onboard ticket inspection but additionally enable innovative spatial validation features introduced by the methodology. The results led to the conclusion that the methodology is effective for estimating journey destinations at the disaggregate level and identifies false positives reliably.

Abstract
The purpose of this paper is to develop a robust methodology to assess municipalities' performance concerning the consumption of resources in residential buildings. The assessment is carried out at a municipal level to inform decision makers about the relative position of their municipalities compared to others. In addition, the factors associated to better levels of municipal performance are identified, and the extent of their effects is quantified. The study uses an enhanced stochastic frontier panel model based on data of energy, water and materials consumption in Lisbon municipalities during the period 2003-2009. The study reveals that the municipalities' performance has remained stable over the years, although there are considerable differences in performance among municipalities. In addition, it is concluded that municipal performance tends to improve with the environmental policy expenditure and scale size, and decline with buildings' age, population density and the proportion of buildings with private ownership.

Abstract
This paper summarizes existing systems and research on information transport services, and proposes a hypothetic scenario for future travellers using public transport. Increased distributed intelligence in pervasive mobile smart devices and in sensor networks in public transport vehicles is enabling a new approach for enhancing the experience of public transport customers. Such environment could be modelled through a distributed multi-agent service system. This paper presents advanced information services already available on such environments, in particular the MOVE-ME smartphone application, and indicates a possible service environment where people's feedback may benefit all transport service stakeholders. Mobile computing and crowdsourcing are key enablers for enhancing user experience in the transport services, and also for enhancing overall public transport services. Better experience leads to increased usage of shared mobility modes, and therefore to more sustainable cities in the future. Concerns about data security, and anonymity of travellers will need to be adequately addressed in the future scenarios presented.

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 public transport system integrates a complex ecosystem, composed not only by transport operators and travellers but also by other services such as schools, firms, restaurants, museums, banks, and public establishments. Therefore, by adopting a holistic point of view, we propose a new service approach linking city services and public transport. This approach consists in partnerships that may include discounts, combined packages, reduced prices, deals and marketing campaigns, targeted to each specific audience. In order to develop these partnerships it is important to analyse the services located around the stations and the public transport usage. We use the city of Porto, Portugal, as an illustrative example and we rely on two data sources: Automated Fare Collection system data and business data points. The analysis of both datasets allowed us to determine the level of concentration of city services located around public transport stations and to identify the types of services that tend to agglomerate near the stations. We were also able to analyse the correlation between the number of travel card validations and the number of services located around the stations. Finally we present a case of a service exposure to different demographic segments.

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.