Pedro Maurício Costa

Exploring Ticketing Approaches Using Mobile Technologies: QR Codes, NFC and BLE

There is a growing interest in integrating public transportation with the smartphone and mobile ticketing provides just that. To do so, different technologies can be used, such as Near Field Communication, Quick Response Codes and Bluetooth Low Energy. This paper explores the possibility of implementing a mobile ticketing solution, with focus on the ticket validation process, using these technologies. They are analyzed and compared at different levels and two possible approaches proposed. Both solutions are presented in terms of infrastructure and maintenance cost, as well as passenger interaction and benefit. The feasibility and performance of the technologies is analyzed and presented in the context of the proposed approaches. As a result, a mobile ticketing solution can be implemented using different technologies, and their choice depends on factors such as the available funds, the intended interaction level, performance and the size of the target audience.

Prediction of Journey Destination in Urban Public Transport

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.

How to support the design and development of interactive pervasive environments

In recent years, the significant advancements in miniaturised computing and pervasive communication networks have paved the way for ubiquitous computing environments. In such environments users interact with systems through novel and implicit methods. In this context, affective computing provides a dimension of interaction, raising a number of opportunities to address not only utilitarian but also hedonic needs. At the same time, a number of challenges arise beyond the technical aspects, that are related to the individual and other societal implications. A review of the main opportunities and challenges is presented, supporting the identification of the main requirements for the design and development of systems in interactive pervasive environments, focusing on an affective loop of interaction. A framework is proposed, identifying main modules and functionality alongside a methodology to instantiate in specific domains of application.

The railway blues: affective interaction for personalised transport experiences

The convergence of personal devices, pervasive communication networks and remote computing has caused a fundamental shift in the user interaction paradigm. Multiple methods have enabled an implicit loop of interaction that goes beyond the traditional graphical interfaces. Human emotion is one of such dimensions, supporting the development of empathic systems. Thus, quality of user experience, a subjective measure, may be defined as the resulting affective state from an interaction, which can be dynamically assessed. In mobile ubiquitous settings, leveraging this affective interaction for providing personalisation and immersive digital services has the potential to significantly impact user experience. This paper investigates the relationship between user affect and experience in the context of urban public transport.

How to Predict Journey Destination for Supporting Contextual Intelligent Information Services

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.

Neurondynamics: A method for neurotransmitter vesicle movement characterization in neurons

Automated tracking of axonal neurotransmitter vesicles is a challenging problem in neuroscience. The present vesicle tracking is typically performed manually over confocal microscopy images. NeuronDynamics is a method designed to automate and speed-up the characterization of global vesicle movement in neurons while yielding high accuracy and precision results (similar or better than expert clinicians). For a set of fluorescent-marked vesicles “films”, Neuron-Dynamics performs a two stage approach: 1) Training: the system asks the user to mark a set of vesicles and the position of the cellular body; 2) Detection & tracking: based on the previous training, the system runs a Bayesian classifier over the image sequence to detect and classify vesicles and their movements (speed and direction). The obtained results were compared to another state-of-the-art method (FluoTracker), and were found greatly higher in accuracy, sensitivity, specificity and precision. Although NeuronDynamics is a semi-automated process, it is significantly faster than manual tracking and can be adapted to be used for similar approaches for other biological samples.