Abstract
Contemporary urban transportation networks are facing challenges to address the growing needs of mobility, all the while improving their economic gains and environmental sustainability. Several studies demonstrate that competitive alternatives to individual private transport are able to address these challenges, such as public transportation services. Thus, the need for optimising their operational efficiency and offer user-centric service delivery arises, with a range of challenges related to the inherent complexity of urban transportation networks as well as the range of dynamic elements involved in such systems. An innovative approach to this problem leverages personal mobile devices in combination with collaborative exchange of information. In this study a system was developed to combine information provided by travellers with data from public transport operators. The result is a rich model of the transportation network that enables the distribution of information in a personalized way and in real-time: the Seamless Mobility solution. Large-scale and expensive infrastructures, such as existing ticketing systems, constitute a threat to such flexibility and traveller access to services. As a result, a distributed architecture was targeted with the goal of integrating personal mobile devices in the infrastructure, with benefits for travellers and transport operators. The proposed solution integrates a broad scope of challenges, including application of secure mobile payments methods, data aggregation from different components and distribution based on relevance techniques. With the implementation of this solution we expect to positively impact the way travellers and transport operators interact, and contribute towards mobility services that are more agile and adequate, taking into account that mobility patterns vary from person to person, seasonally, and even throughout a day. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
This paper proposes an ontology-based approach to visualizing urban mobility data. Our approach, which is in ongoing development, is centered in a visualization-oriented urban mobility ontology that is used to semantically characterize data and visualization techniques. We present a practical application to a public transportation network of the city of Porto, Portugal. We address how semantics can empower and facilitate tasks like automatic recommendation of visualization techniques, and definition of a data filter based on passengers' journey patterns.

Abstract
The proliferation of new technologies is revolutionizing the public transport sector, allowing Operators to replace complex and expensive infrastructures by travellers' mobile devices and online management channels and platforms. This paper aims to present the Seamless Mobility platform, a disruptive solution based on these new channels, which main goal is to facilitate and promote public transport usage for travellers, as well as reducing operational costs for public transport companies. The Seamless Mobility platform integrates three main components: (i) mobile payments, (ii) route planner, and (iii) social network. The payment component is based on the pay-as-you-go concept with check-in and check-out requiring the reading of the corresponding QR Code station. The route planner combines information from published schedules with real-time information to identify the nearest stops, the next departures, or the best route for a scheduled trip. The social network component allows real time sharing among travellers of public transport information, related to several aspects of the service (e.g. noise, skilfulness of drivers). To test the concept, a mobile application, called OneRide, was developed. This application was tested by users in real environment, in the city of Porto, Portugal. The results show that users considered the system extremely useful, since it is more convenient than traditional systems. It was also clear that users valued the integration of additional and complementary services with mobile payments, such as information about their journey, maps and schedules. Regarding the social component some users found it difficult to understand the concept, but once they understood they considered it very useful. The use of the QR Codes to perform the payment has shown to be one of the main challenges to be addressed, since lighting conditions, position and distance to the QR Code influences the reading process. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Nowadays, mobile phones are ubiquitous systems of our society. Nevertheless, the adoption of this technology to perform mobile payments, namely in public transport, was only implemented in a few number of transport networks. Thus, this paper aims to understand which are the main factors that may influence the adoption of mobile payments in public transport. For this purpose, a survey was applied to different groups of population. The study was conducted on the public transport networks of a medium-sized metropolitan area (Oporto-Portugal) and of a big-sized metropolitan area (Beijing-China). The evaluation results of the current services of purchase and validation of public transport tickets almost never show significant statistical differences (p>0.05) for the traditional variables used by the literature. This is particularly true for age. Nevertheless, some mobility factors can sometimes play an important role in the assessment of ticketing systems. Moreover, although the high differences between the ticketing systems in both cities, Chinese and Portuguese have a similar opinion about the systems implemented in their cities. Still, Chinese reveal a higher motivation to adopt the new ticketing system. In general, such system is greatly accepted by the respondents and the potential market is expected to be high (30-68%). Although this technology cannot replace the traditional systems, it can contribute to increasing the overall efficiency of the transport networks. The improvement of the passengers' appraisal, the reduction of operational and the maintenance costs of transport operators are the network impacts most expected. Convenience and time saving are the main advantages mentioned while questions about privacy, interaction and reliability are stated as the main concerns to adopt it. (C) 2017 The Authors. Published by Elsevier B.V.

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
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.