Abstract
Adaptive Cruise Control (ACC) is a system that maintains driver-selected speed and headway to a preceding vehicle. The system presents some limitations that are, in part or totally, unknown to the users. Hence, many drivers exhibit a rudimentary mental model of the system and place excessive trust in the device. As a consequence, negative effects on road safety can easily occur. However, to date, many studies conducted on ACC have comprised participants who had never used ACC previously. Therefore, there is limited knowledge of how ACC affects the driving performance of experienced users of the system. To shed light on this point, twenty-six participants, divided into two groups (ACC users and non-users) drove twice in the simulated environment (once with the ACC and once manually). During both drives, the participants experienced a critical situation (stationary vehicle stopped in the cruising lane of the highway). The results show that negative behavioural adaptations to the ACC resulted from the usage of the system with regard to the critical situation: the risk of collision during the driving with ACC was increased compared with the manual driving for both groups of drivers. Besides, the research stresses the negative large correlation between the driver's mental model of ACC operation in the critical situation and the safety margins maintained by the ACC users during the same situation. Finally, it was found that the drivers' trust in the system does not have an influence on the drivers' behaviour during the trial with the ACC.

Abstract
Energy planning is a complex process involving multiple and conflicting objectives with many agents able to influence decisions. This complexity is frequently addressed with the use of multicriteria tools, relying on a set of criteria and different methods to aggregate all the information in a final ranking of the available alternatives. This paper describes the application of a multicriteria decision tool for the analysis of Portuguese electricity scenarios. A set of criteria is proposed aiming to include social, economic, environmental and technical aspects. Criteria weighting was directly addressed considering 5 approaches: equitable weights, financial, technological, social and environmental perspectives. Results indicate that close to 100% RES scenario is the best option under a social perspective, base scenario represents the best option on a technical approach and scenarios relying on natural gas and wind power units are the best options for the electricity system under equitable weights, economic and environmental approaches.

Abstract
We present an Information Retrieval tool that facilitates the task of the user when searching for a particular information that is of interest to him. Our system processes a given set of documents to produce a graph, where nodes represent documents and links the similarities. The aim is to offer the user a tool to navigate in this space in an easy way. It is possible to collapse/expand nodes. Our case study shows affinity groups based on the similarities of text production of researchers. This goes beyond the already established communities revealed by co-authorship. The system characterizes the activity of each author by a set of automatically generated keywords and by membership to a particular affinity group. The importance of each author is highlighted visually by the size of the node corresponding to the number of publications and different measures of centrality. Regarding the validation of the method, we analyse the impact of using different combinations of titles, abstracts and keywords on capturing the similarity between researchers.

Abstract
Intelligent transportation systems based on automated data collection frameworks are widely used by the major transit companies around the globe. This paper describes the current state of the art on improving both planning and control on public road transportation companies using automatic vehicle location (AVL) data. By surveying this topic, the expectation is to help develop a better understanding of the nature, approaches, challenges, and opportunities with regard to these problems. This paper starts by presenting a brief review on improving the network definition based on historical location-based data. Second, it presents a comprehensive review on AVL-based evaluation techniques of the schedule plan (SP) reliability, discussing the existing metrics. Then, the different dimensions on improving the SP reliability are presented in detail, as well as the works addressing such problem. Finally, the automatic control strategies are also revised, along with the research employed over the location-based data. A comprehensive discussion on the techniques employed is provided to encourage those who are starting research on this topic. It is important to highlight that there are still gaps in AVL-based literature, such as the following: 1) long-term travel time prediction; 2) finding optimal slack time; or 3) choosing the best control strategy to apply in each situation in the event of schedule instability. Hence, this paper includes introductory model formulations, reference surveys, formal definitions, and an overview of a promising area, which is of interest to any researcher, regardless of the level of expertise.

Abstract
Large scale social networks streaming and visualization has been a hot topic in recent research. Researchers strive to achieve efficient streaming methods and to be able to gather knowledge from the results. Moreover treating the data as a continuous real time flow is a demand for immediate response to events in daily life. Our contribution is to treat the data as a continuous stream and represent it by streaming the egocentric networks (Ego-Networks) for particular nodes. We propose a non-standard node forgetting factor in the representation of the network data stream. Thus, this representation is sensible to recent events in users networks and less sensible for the past node events. The aim of these techniques is the visualization of large scale Ego-Networks from telecommunications social networks with power law distributions.

Abstract
This paper proposes an offering strategy for a wind power producer (WPP) that participates in both day-ahead (DA) and balancing oligopoly markets as a price maker. Penetration of demand response (DR) resources into smart grids is modeled by intraday demand response exchange (IDRX) architecture. A bilevel optimization framework is proposed based on multiagent system and incomplete information game theory. Modeling the WPPs in high penetration of wind power as price makers can reflect the capability of this market player to directly affect the market prices. Simulation results indicate that the price-taker model ofWPP is not accurate for WPPs that have significant market shares. By comparing the results obtained from modeling the WPPs as price makers with the ones as price takers, it can be concluded that WPPs have the market power not only to increase the prices of both DA and balancing markets, but also to reduce the amount of DR through IDRX market mechanism.