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Modeling and simulation tools are being increasingly acclaimed in the research field of autonomous vehicles systems, as they provide suitable test beds for the development and evaluation of such complex systems. However, these tools still do not account for some integration capabilities amongst several state-of-the-art Intelligent Transportation Systems, e.g. to study autonomous driving behaviors in human-steered urban traffic scenarios, which are crucial to the Future Urban Transport paradigm.In this paper we describe the modeling and implementation of an integration architecture of two types of simulators, namely a robotics and a traffic simulator. This integration should enable autonomous vehicles to be deployed in a rather realistic traffic flow as an agent entity (on the traffic simulator), at the same time it simulates all its sensors and actuators (on the robotics counterpart). Also, the statistical tools available in the traffic simulator will allow practitioners to infer what kind of advantages such a novel technology will bring to our everyday's lives. Furthermore, an architecture for the integration of the aforementioned simulators is proposed and implemented in the light of the most desired features of such software environments.To assess the usefulness of the platform architecture towards the expected realistic simulation facility, a comprehensive system evaluation is performed and critically reviewed, leveraging the feasibility of the integration. Further developments and future perspectives are also suggested.

The evacuation of complex buildings is a challenge under any circumstances. Fire drills are a way of training and validating evacuation plans. However, sometimes these plans are not taken seriously by their participants. It is also difficult to have the financial and time resources required. In this scenario, serious games can be used as a tool for training, planning and evaluating emergency plans. In this paper a prototype of a serious games evacuation simulator is presented. To make the environment as realistic as possible, 3D models were made using Blender and loaded onto Unity3D, a popular game engine. This framework provided us with the appropriate simulation environment. Some experiences were made and results show that this tool has potential for practitioners and planners to use it for training building occupants.

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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 modeldriven 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.