Short-term traffic prediction provides tools for improved road management by allowing the reduction of delays, incidents and other unexpected events. Different real-time approaches provide traffic managers with varying but valuable information. This paper reviews the literature regarding model-driven and data-driven approaches focusing on short-term realtime traffic prediction. We start by analyzing real-time traffic data collection, referring network state acquisition and description methods which are used as input to predictive algorithms. According to the input variables available, we describe common and useful traffic prediction outputs that should contribute to understand the panorama verified on a road network. We then discuss metrics commonly used to assess prediction accuracy, in order to understand a standardized way to compare the different approaches. We list, detail and compare existing model-driven and data-driven approaches that provide short-term real-time traffic predictions. This research leads to an understanding of the many advantages, disadvantages and trade-offs of the approaches studied and provides useful insights for future development. Despite the predominance of model-driven solutions for the last years, data-driven approaches also present good results suitable for Traffic Management usage.

This paper intends to analyze the performance of an electric bus for three different routes in the city of Oporto. The objective is to study the correlation between the type of route a bus travels and the amount of energy it spends in performing it. Besides energy, the potential of the recovering part of it through regenerative braking will also be discussed. It is expected that long and demanding routes, e.g. those encompassing meandering paths, will make the bus use more energy. To accomplish these objectives, a simulation platform was built upon both a nanoscopic representation of the electrical bus and its microscopic interaction with the road traffic network while performing different journeys through specific routes. Simulation results are compared and discussed, as well as are directions for future work identified

Serious Games are increasingly used as a training and educational tool. Fire keeps claiming a high number of victims. Some authors allege failures during the evacuation process as the main cause that contributes for that number. Fire drills are used to train buildings' occupants for emergency situations. However, fire drills' participants often have prior knowledge of their schedule and as result they are not as focused as they should. Moreover, fire drills require the mobilization of some resources, leading to financial costs. Performing a fire drill will always affect the normal functioning of the place where they take place; for this reason, sometimes they are not performed at all. In some special locations, like hospitals, fire drills are unsuitable. Emergency planning is crucial to prevent and minimize damage and victims. Albeit the improvement on safety measures, many occupants and employers in this type of buildings still lack adequate knowledge and training on how to behave in such hazardous situations. EVA is presented as a possible solution, which is based on the concept of Serious Game that can be used as a training tool for healthcare professionals. A preliminary prototype was developed. A sample of 20 subjects was selected to test it giving promising results. Results also showed that users who had training in fire prevention appear to perform better in the course. Moreover, participants who regularly play computer games overcame easily the game challenges. Further research and development are still to be explored, which are discussed in the concluding part of this paper.