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Public opinion is nowadays a valuable data source for many sectors. In this study, we analysed the transportation sector using messages extracted from Twitter. Contrasting with the traditional surveying methods that are high-cost and inefficient used in transportation sector, social media are popular sources of crowdsensing. This work used BERT embeddings, an unsupervised pre-trained model released in 2018, to classify travel-related terms using tweets collected from three distinct cities: New York, London, and Melbourne. In order to understand if a simple model can have a good performance, we used unigrams. A list of 24 travel-related words was used to classify the messages. Popular words are train, walk, car, station, street, and avenue. Between 3% to 5% of all messages are classified as traffic-related, while along the typical working hours of the day the values is around 5-6%. A high model performance was obtained, with precision and accuracy higher than 0.80 and 0.90, respectively. The results are consistent for all the three cities assessed.

Home Energy Management Systems (HEMS) stem as a promising solution to improve sustainable patterns of energy consumption within Smart Homes, which could significantly contribute to achieving the United Nation's sustainable development goals. In this paper, we present a resource-based reference model of such a system, which is based on the Multi-Agent systems (MAS) paradigm. Our model builds upon existing distributed demand-side management strategies, where agents are involved in a proposal-based business process leading to a schedule that organises the activation of the Smart Home's appliances. The proposed model serves as a starting point for implementing the Home Energy Management System and for future simulations that will validate the effectiveness of this solution.

Every day we hear about the current state of the climate and how it is degrading very quickly. It is urgent to study ways in which people can change and improve their daily lives towards more sustainable habits. This paper studies graph-based simulation scenarios, focusing on both human and infrastructure characteristics, which could lead to a potential reduction in CO 2 emissions. We compare alternative policies with an upper-bound base scenario, in which people use their own cars for their sole use. Most of these scenarios are based on Dijkstra’s shortest path algorithm, while others serve to underlie our reflections on the feasibility of using minimal spanning trees to solve the problem. The zone selected to illustrate this study is the coastal area of Espinho, a city in Portugal. It is shown that the scenarios with carpooling starting on the cut vertices of a tree generated by linking Dijkstra’s shortest paths of each agent yield the best results. This approach leads to a reduction in carbon emissions of between 25% and 30%.

The COVID-19 outbreak has proven to be a challenge for most communities, requiring them to adapt to a newfound reality. Cities need now to accommodate the circulation of their populations in a safe manner, dealing with economic repercussions, and avoiding to oversaturate the countries’ healthcare facilities. So far, the latter has happened with dramatic consequences in terms of loss of human lives. In this context, we propose a social simulation meta-model suitable to represent the complex socio-technical system of a campaign hospital, created to support existing healthcare facilities as a response to the demands created by the coronavirus pandemic. With this model we intend to support the analysis of social coordination policies towards the improvement of a given set of characteristics of the system. By considering both technical and social dimensions, we expect to gain insights into how certain aspects such as the collaborativeness of patients or the nature of staff might affect the healing speed of patients and, similarly, the efficiency of the campaign hospital. Ultimately, all emergent behaviour should provide useful insights allowing for the identification of key social practices influencing its performance.

Cooperative Intelligent Transport Systems (C-ITS) could benefit from the vehicle-to-infrastructure (V2I) principles to improve traffic performance, obtaining user/local data from vehicles and providing them back with information tailored from the whole system altogether. In this work, we explored the strategy of conveying recommendations to drivers through buses that are in front of them and equipped with a variable message sign (VMS) on their rear. We analyse the impact of such a strategy on the system equilibrium, using the Simulation of Urban MObility (SUMO) microscopic simulation suit. There is a correlation between the number of buses and the percentage of informed decisions and, therefore, it is crucial to consider the frequency of the bus services and the communication efficiency between the road-side (RSU) and on-board (OBU) units, as well as articulate the RSU location with traffic infrastructure, in order to minimize transmission failures and obtain a better traffic performance.

In recent years, we have seen a wide spread of gamification as a tool to motivate individuals into following a considered action or set of actions. However, there are not so many methods of evaluating the effects of its implementation. This paper focuses on the particular case of promoting carpooling in a closed community. We show that the combination of contributions from a set of gamification elements can decrease CO 2 emissions in about 11%. Additionally, the individual contributions of each element are ranked, showing which elements contribute more and less to the reduction of carbon emissions. Regarding this matter, statistics are considered the best element, with the potential to promote a reduction of CO 2 emissions in about 1.515%.

Nowadays energy demand is an issue regarding the vitality of cities and the welfare of citizens. The exponential growth of the cities and the consumers’ demand obliged the energy producer to find a modern solution to overcome these problems. The solution was found in decentralised structures all connected through the Internet and capable of allowing all actors in the system to interact together. In this study, the effect on the smart grid’s energy demand of different numbers of consumers, their behaviour and the energy price variations will be discussed. A few consumers’ behaviours will be illustrated and taken into account, how they react to the energy provider’s price schemes, impelling them into making a decision on whether to shift or not their demand according to their characteristic parameters and show how their expenditures and incomes for the electricity market operator change.

Traffic congestion is an issue regarding the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and make different decisions towards transportation modes. Modification and improvement of these systems affect the commuters’ perspective and social welfare. In this study, the effect of road flow equilibrium on commuters’ utilities with different types of transportation modes will be discussed. A simple network with two modes of transportation will be illustrated and three different cost policies were considered to test the efficiency of reinforcement learning in commuters’ daily trip decision-making regarding time and mode. The artificial society of agents is simulated to analyse the results.

Advanced Traveller Information Systems (ATIS) have seen a steady increase in popularity in the last decades among urban users. By providing travellers with relevant information, these systems have the ability to considerably improve traffic flow, but are limited by their penetration rate among the city’s population. In this work we put forward HERMES1, a tool to facilitate the evaluation of road networks through simulation with different ATIS and with different levels of information percolation among users. The usefulness of such a tool stems from the quantification of the extent to which an ATIS can improve total travel time and road utilisation by means of experimenting with simulation scenarios. This tool operates at a mesoscopic level, in which single users may be represented independently from each other, nonetheless are rather considered to operate at their macroscopic interactions with the network. We also illustrate example use cases for the tool on the motorway network between the Portuguese cities of Lisbon and Porto.

This paper proposes the application of market-based mechanisms to establish cooperative behavior within traffic scenarios involving autonomous vehicles. The aim is to understand the suitability of commonly used auction rules as a potential mechanism for tactical-level collective decision-making in platoon applications considering near-reliable communications. As a proof of feasibility, two auction clearing rules are compared, namely the first-price and the second-price sealed bid auctions. Both rules are tested according to their impact on coalition welfare and communication. To realistically assess the system performance, we designed an integrated simulation platform composed of an agent-based platform, a microscopic traffic model, and a vehicular network simulator. Results show the viability of two rules to maintain high satisfaction among platoon members, that can lead to stable formations and consequently better traffic conditions. From a communication perspective, a non-negligible delay is present, and should be taken into account when implementing an auctioning mechanism for real-world deployment.

A solution for an optimal robotic soccer strategy is yet to be found. Multiple agents interacting in an environment with continuous state and action spaces is a recipe for machine learning lethargy. Fortunately, due to the increasing hardware performance and new algorithms, the area of reinforcement learning is growing considerably. However, simulating the environment for strategy purposes is not following this trend. Several simulators are available, including those used in major soccer competitions (e.g. RoboCup). However, no option combines a good repository of teams with a simple command set that abstracts low-level actions. To clarify this problem, we surveyed the most promising simulators and proposed an extension for the well-established Soccer Server. The objective was to simplify the process of learning strategy-related behaviors through automated optimization algorithms. The results have shown a clear advantage in using the extension to improve the agent's performance. This work contributes to the development of future strategies related with RoboCup or other soccer competitions. Despite the good results, there is space for improvement in computational efficiency and behavior diversity.

Recent research has shown that by optimizing lane changing maneuvers and making vehicles travel much closer to each other it is possible to achieve a significant reduction in traffic congestion – a severe problem in all major cities. The issue of lane-merging was approached in the context of autonomous vehicles, capable of inter-vehicle communication and grouped into platoons. Solutions based on the use of negotiation techniques were explored by modelling and performing simulations in SUMO (Simulation for Urban Mobility). This paper demonstrates the benefits of the use of platoons in traffic flow, across a range of metrics, and proposes two methods of negotiation for vehicles entering a lane to merge into a platoon.

Considering an environment that consists of several services, applications and platforms, each present entity produces a certain amount of data. With so many sources of data, there are a number of things bound to exist: different formats of information, redundancy and no consistent standards of information. In environments as these, the collaboration between different entities creates an opportunity for innovation, where data interoperability allows for the re-use of information, the possibility of different services taking advantage of other third-party sources and the development of new businesses from existing information. This, however, is only possible if there is some sort of interoperability between the data, a way for it to be transmitted from entity to entity, always with the possibility of creating value with its manipulation and consumption. This paper exposes the work done in the development of a platform focused on data, looking into its forms of representation and how to solve the problems caused by the ever existing necessity of data interoperability between systems. The possibility for maintaining and creating Open Data Ecosystems is also analysed in the scope of the proposed platform.

With all the advances witnessed in technology, data visualization was bound to suffer a revolution. However, visualization tools still have a very complex interface, especially in simulation models such as macroscopic traffic simulators. In this paper, our aim is to provide a clean, easy-to-read and adaptable visualization tool, within a novel macroscopic traffic simulator. The choice of visualization metaphors came from extensive research in the broader field of fluid mechanics. From its analysis, we were able to determine the metaphors that better capture the different aspects accurately and choose those which could be more adaptive according to the interests of the users and the respective simulation analysis purpose. As part of a project dedicated to devise a novel macroscopic traffic simulator following the concept of simulation-as-a-service, we need an appropriate, intuitive and user-friendly interface so simulation data can be easily interpreted on web browsers. As a result, the user has total control over the visualization of the data, and can chose the combination that best suits the required needs. Between the colour of lines, their thickness or the “heat” of the traffic, added to the choice of the layer map, we provide a very adaptive and effective tool to represent and study traffic.

The management and maintenance of road infrastructures demand the use of a tremendous amount of data about their maintenance history and current status. One very characteristic of this information is its geographic nature, which suggests that Geographic Information Systems (GIS) are appropriate to facilitate the way we handle it. However, the access to such information nowadays is challenging because of numerous reasons: some data is still mainly stored on paper; databases are out of date; managing scarcely existing records and creating new ones is quite a laborious and time-consuming task; field inspections require human resources and are expensive; and so forth. Thus, we need to make smarter the way management and maintenance of road infrastructures are performed. Some promising technologies appeared in the last few years to overcome a number of the identified issues. This paper presents and discusses on the most prominent work efforts aiming at more intelligent management of the city infrastructures, specially focusing on transport networks. Several of such efforts use Geographic Information Systems, Building Information Modelling (BIM), Internet of Things (IoT), and Virtual/Augmented Reality (VR/AR) technologies. Thus, this study emphasizes on the GIS-BIM-IoT and GIS-BIM-VR/AR integrations showing the possibilities and potentials when these technologies work together.

The physical environment of a community has been proven to have effects on the mental and physical state of a population. As such, the extraction of Urban Indicators (UI) that evaluate the effects of urban development is essential to assert relationships between the surrounding environment and the well-being of a society. Such a relationship, for example, would be the role of green areas in a city on the prevalence of obesity in its population. In addition, these indicators can contribute to the identification and preventive action in risk situations. For instance, a very degraded area with too much waste accumulated may pose serious risks to public health. However, the traditional methods for UI extraction, particularly in the case of physical indicators, are limited due to the lack of standardized data organization and the subjectivity of self-reported responses, while generally being highly resource-intensive and costly. This work aims to create a methodological approach that is capable of applying Computer Vision to automate the extraction of UI, overcoming the limitations of the traditional approaches. This approach takes advantage of tools that offer remote visualization of locations at low cost. Its success depends on the accurate identification of physical urban indicators that can be extracted from an image, and on choosing appropriate Computer Vision techniques to provide the most precise results for such an analysis.

This project focuses on the development of a video analytics processor for detection and classification of vehicles on motorways. The motivation for this project comes from the need to have a plug-and-play solution to analyse traffic, as most of the existing solutions require training some sort of structure to recognise objects on the scene. Such a module can work as a data input for traffic management systems in addition to more traditional sensors such as the magnetic loop detectors. We also present a novel approach to the vehicle classification problem based on the use of a fuzzy set. To illustrate the proposed approach, the detection and classification algorithms implemented were tested with different cameras in different scenarios, showing promising results.

Since human societies first reached a level of stability and social organization high enough to make waste collection a concern of local governmental authorities, a lot of time and resources have been invested into the matter. With the ascension of the so-called Smart Cities, many unexplored opportunities are brought up in this context. The present paper proposes a software architecture to be implemented in the context of Smart Cities. A platform for the research, simulation and implementation of route generation algorithms, that challenges the traditional models and that makes the best use of the technologies available today, such platform aims to function as an implementation of an Artificial Transportation System with the goal of suppressing the existent flaws and gaps in today's systems, be it for economic, environmental, sustainability, time-efficiency or even health related areas. The architecture's initial draft is explained and its results are presented and analysed.

Knowledge mining from the historical traffic big data is absolutely necessary for future intelligent transportation system (ITS) and smart city. Mining traffic data is a challenging task that can be used for traffic forecasting and improving traffic flow. In this paper, we explore and analyse the historical traffic big data to extract the informative patterns. Three years (2013 to 2015) real traffic data was collected from the city of Porto, Portugal. We developed a Java based traffic data observation (TDO) tool for visualising traffic data, which can filter and extract expressive patterns from the traffic big data based on input features. Then, graphs are generated by TDO from the traffic data to find the historical averages of traffic flow. Finally, we have applied regression models: Linear Regression, Sequential Minimal Optimisation (SMO) Regression, and M5 Base Regression Tree on the traffic data to find annual average daily traffic (AADT) and compare their results. Also, we have used regression trees to find the traffic patterns. The goal is to find the abnormal traffic patterns from the historical traffic big data and analyse them to improve the traffic management system (TMS).

In the information security field, varions algorithms are applied to provide users with a variety of security services. Although many of the algorithms are implemented in software, there is a rapidly growing demand for hardware implementations. The BlaMka algorithm is a cryptographic sponge algorithm, used in password hashing schemes (PHS) such as Lyra2 and Argon2, which are algorithms that generate a pseudo-random sequence of bits from a particular secret value. BlaMka was proposed as a modification of Blake2b algorithm by including multiplications in some stages of its implementation, specifically in its permutation function, aiming to reduce its performance in hardware platforms. As far as we are concerned, there is no hardware implementation for the BlaMka algorithm. Thus, this work presents a hardware implementation for the permutation function of BlaMka, which is the structure that contains multiplications, with some different architectures to address the trade-off between latency, area and power, besides verifying the impact of the inclusion of multiplications in its performance compared with a hardware implementation for the permutation function of Blake2b. All the circuits presented herein were described using VHDL language and synthesized for ASICs using the tools and the 90nm cell library provided by Synopsys. Combinational and sequential circuits were synthesized covering a range of possible choices taking into account the throughput, and the costs for area and power. The results for throughput ranged from 0.907 Gbps to 2.426 Gbps, area utilization ranged from 14.13 kGE to 52.82 kGE, and power consumption ranged from 0.384 mW to 98.462 mW. The results were compared to an implementation of the permutation function of Blake2b, verifying that the performance decrease is quite marked when multiplications are included in the permutation function.

Taxi sharing services have been increasing in the past few years in several countries. In this paper we propose a solution to taxi sharing problem based on Vehicle Routing Problem (VRP) with Temporal Restriction. We approach the problem in a static way, for testing purposes. Two metaheuristics were used to determine the best travel plans for taxi sharing service, taking into account the pickup and delivery time of each passenger. Tabu Search (TS) and Simulated Annealing (SA) were used to solve the problem, as literature review suggested, presenting good results. Four test scenario were used with 10, 20, 30 and 40 passengers, knowing their starting location. Analysis of solutions found by TS and SA are compared to the average cost to conclude which is the best travel plan to be used by taxi sharing service.

In the contemporary world, simulation has been gaining a huge importance due to the cost reduction it promotes on many different areas. This paper proposes a validation methodology for the simulation model of an electric bus. This methodology consists in applying the sensitivity analysis method to assess the robustness of the model, compared to real data collected for different scenarios. Data were collected from the operation of the real bus and used in the validation process, which could demonstrate that the model's outcomes are valid within a range of changes in the parameters analyzed, namely number of passengers and accessories energy consumption.

This work proposes a novel framework for the development of new products and services in transportation through an open innovation approach based on automatic content analysis of social media data. The framework is able to extract users comments from Online Social Networks (OSN), to process and analyze text through information extraction and sentiment analysis techniques to obtain relevant information about product reception on the market. A use case was developed using the mobile application Uber, which is today one of the fastest growing technology companies in the world. We measured how a controversial, highly diffused event influences the volume of tweets about Uber and the perception of its users. While there is no change in the image of Uber, a large increase in the number of tweets mentioning the company is observed, which meant a free and important diffusion of its product.

Private individual transportation is becoming cumbersome and expensive, as urban traffic turns more chaotic, fuel prices increase and the effects of pollutant emissions become evident. Public buses are an attractive approach to reducing the cars in use, as they mostly depend on preexistent infrastructure. Making these buses electric would mean even less tailpipe emissions and cheaper consumption costs, when compared to conventional vehicle fleets. However, fully electric bus fleets can prove disadvantageous. We can tackle this with a more conservative approach - using mixed bus fleets, comprised by both electric and conventional buses. This work intends on studying how to obtain a good balance of the different vehicle typologies in the fleet. To fulfill these goals, real data of a bus network in Porto, Portugal, is studied and an evolutionary algorithm devises mixed fleet arrangements, with a brief sensitivity analysis giving us an overview of how to improve our results. As a means of decision support, this work contributes not only with an approach to configure optimized mixed bus fleets, but also with general considerations for managing public transit with electric vehicle fleets.

It is with great pleasure and excitement that I welcome you all to Rio and to IEEE ITSC 2016! In its 19th edition and having travelled many countries already, I'm highly honoured to lead the organization of the prestigious flagship conference of IEEE Intelligent Transportation Systems Society, held for the first time in the Southern Hemisphere, more specifically in Brazil. What a great historical moment to our community!

Over the last decade, the impressive technological advancement around artificial intelligence have encouraged and stimulated much research on driverless cars. Inevitability, such novelty casts huge speculation on its effective deployment in the urban reality. The viability issue of cybercars demands appropriate research so as to infer and validate upcoming impacts at different levels, especially the mobility level, and address new performance measures intrinsic to this novel transportation paradigm. Therefore, this paper presents a short overview perspective about the definitions around the so-called autonomous vehicles. Additionally, as long as multivariate modelling is concerned, this position paper contributes with insights that span some of the future problematics and research questions regarding the automated vehicles topic.

Automatic Vehicle Location (AVL) is becoming an important tool in Intelligent Transportation Systems (ITS) in the past few years, as it is an effective way of collecting and transmitting data regarding the vehicle's trip for real-time or future use. A methodology for analyzing the state of the art regarding the application of these systems is proposed in a form of a systematic literature review, by identifying and systematizing possible transportation network performance metrics that can be calculated or predicted using GPS-based AVL systems and inferring tendencies observed throughout the literature regarding techniques used and sensor data source and usage. As a result of this research, several performance metrics were identified, with Travel Time and Average Speed being the most recurrent ones. The conclusions reveal an increase in the number of publications and research projects regarding this topic over the years, as well as a promising potential of this type of technology, with buses and taxis being the most used probe vehicles.

Simulation is a computer-based experimentation tool suitable to determine the efficacy of a previously untried decision. In this paper, we present a model of climate change. The goal behind this project is to provide a test-bed to evaluate theories related to the Earth system so as to test and evaluate metrics such as greenhouse gases and climate change in general. The proposed approach is based on a multi-agent model which has as input a representation of nature and as output the changes that will occur on Earth within a given instant of time. Most views about climate change do not take into account the real severity of the subject matter; however, the present perspective is given in a way so as to make non-experts aware of the risks that are threatening life on Earth. Just recently, the general population has developed considerable sensitivity to these issues. One important contribution of this work is to use agent-based modeling and simulation as an instructional tool that will allow people to easily understand all aspects involved in the preservation of the environment in a more aware and responsible way.

Elevation data is very important for precise electric vehicle simulation. However, traffic simulators are often strictly two-dimensional and do not offer the capability of modelling urban networks taking elevation into account. In particular, SUMO — Simulation of Urban Mobility, an often used microscopic traffic simulation platform, relies on urban networks previously modelled with elevation data in order to provide access to this information during simulations. This work intends on tackling the problem of how to add this elevation data to urban network models — in particular for the case of the Porto urban network, in Portugal. With this goal in mind, a comparison between different altitude information retrieval approaches is made and a simple tool to annotate network models with altitude data is proposed. This paper starts by describing the methodological approach followed to develop the work, then describing and analysing its main findings. This description includes an in-depth explanation of the proposed tool. Lastly, this paper reviews some related work to this subject.

Recently, many studies in the literature have been intensified in the scope of mobility services, presenting new technological solutions that can minimize problems, such as traffic congestion and the emission of greenhouse gases resulting from the population growth in large cities. Some services, namely shared services, are mainly dedicated to trying to encourage people to use transport that can be shared. In this paper, we propose a systematic literature review protocol oriented to shared transportation, so as to support bibliographical reviews by researchers on this domain. From the proposed systematic review methodology, we present preliminary results of a survey conducted on shared services.

Interest in robotics field as a teaching tool to promote the STEM areas — Science, Technology, Engineering and Mathematics has grown in the past years. The search for costless solutions to promote robotics is a major challenge and the use of real robots always increases associated costs. An alternative to this is the use of a simulator. The construction of a simulator related with the Portuguese Autonomous Driving Competition using Gazebo as 3D simulator and Robotics Operating System (ROS) as a middleware connection to promote, attract, and enthusiasm university students to the mobile robotics challenges is presented. The proposed simulator focus on the autonomous driving competition task, such as semaphore recognition, localization, and motion control. An evaluation of the simulator is also performed, leading to an absolute error of 5.11% and a relative error of 2.76% on best case scenarios relating to the odometry tests and an accuracy of 99.37% regarding to the semaphore recognition tests performed.

In the early twenty-first century, social networks served only to let the world know our tastes, share our photos and share some thoughts. A decade later, these services are filled with an enormous amount of information. Now, the industry and the academia are exploring this information, in order to extract implicit patterns. Twitter Jam is a tool that analyses the contents of the social network Twitter to extract events related to road traffic. To reach this goal, we started by analysing tweets to know those which really contains road traffic information. The second step was to gather official information to confirm the extracted information. With these two types of information (official and general), we correlated them in order to verify the credibility of public tweets. The correlation between the two types of information was done separately in two ways: the first one concerns the amount of tweets in a certain time of day and the second on the localization of these tweets. Two hypothesis were also devised concerning these correlations. The results were not perfect but where reasonable enough. We also analysed tools suitable for the visualization of data to decide what is the best strategy to follow. At the end we developed a web application that shows the results, to help the analysis of results.

Nowadays there is too much data available. This is because almost every electronic device around us generates data. Usage of this resource is a must if one want to improve transportation. Having roads a variety of sensors installed, their data readability, comprehension and usage is of vital importance for a good traffic analysis. Data integration is needed to analyse information from these different sensors. Having this in mind, it is also known that this task is difficult if done manually, necessitating appropriate research and new techniques that can simplify such a task and preferentially automate it. The concept of ontology is particularly suitable to address data integration issue while maintaining their original meaning and representation. This paper explores the ontology concept so as to leverage data integration through a service-oriented architecture envisaging applications in transportation systems. Such a perspective allows potential clients to easily access heterogeneous data accounting neither for their original schema nor for their location and origin. To illustrate the approach, a prototype was implemented, which uses data from GPS logs, network topology from OpenStreetMap and counts from inductive loops.

Existing approaches to traffic assignment focus mainly on approximating the user equilibrium. However, nowadays, with the increasing number of traffic information reaching drivers, traffic authorities have a unique opportunity to try to recommend route choices that are as much as possible aligned with the system optimum. In this paper, we formulate traffic assignment as a multi-objective optimization problem and engage an evolutionary approach to find route solutions for all users in the network. The aim is to discover a good approximation of an optimal distribution of vehicles to alternative routes between their origin and destination, from the perspective of the overall system, while still considering individual needs. Several multi-objective models are defined for this purpose and integrated in a nondominated sorting genetic algorithm. Computational experiments performed support the ability of the proposed approach to detect efficient route assignments in terms of network performance, while also considering the user perspective. efficient route assignments in terms of network performance, while also considering the user perspective.

The crescent diversity and heterogeneity of vehicles increases the importance of simulation platforms that can reproduce missions using several kinds of vehicles. A platform is being developed at LIACC for this purpose, using Microsoft Flight Simulator X as the simulation engine. Because this simulator has some constraints involving its active simulation area around the player's central aircraft, a strategy needed to be created to deal with the problem. A proof of concept was implemented, using two different instances of the simulator and moving the aircraft seamlessly from one simulator to another, thus augmenting the available simulation area and distributing the simulation workload by several network-connected machines.

This paper focuses on the process of developing a framework that features Multi-Agent System and Fire Simulation in a game engine, resulting in a gaming infrastructure which may be played as a serious game to train users for emergency situations. Scenarios are generated dynamically with different types of characters controlled by the computer, Non-Playable-Characters (NPC). The computer controlled firefighters will go for rescue in an indoor fire scenario, while the player is able to either help them by playing a fellow firefighter or just watch the NPCs solve the situation. These NPCs are made using a Unity3D plug-in named RAIN to speed up the development process, which helps modelling the perceptions of the agents and their behaviours. These processes will be described and the result is a rescue simulation tool showing advantages in debugging behavioural models, which leverages the creation of these types of simulations. The less the player realizes that a firefighter is in fact an NPC being treated as another human player, the more likely the player is expected to behave socially. The current version of the simulation environment is available for download and free to play so readers can test and experiment with the implemented framework.

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.

Many sophisticated Information Systems (IS) fo- cusing on management and supervision problems in the trans- portation arena exist in the contemporary world. Road traffic management systems, rail traffic management systems and many more are here to face everyday challenges. However, in spite of a huge progress in this area, systems that combine multiple transport modes for management purposes are a very few. This article proposes a solution for the monitoring of the operational state of multimodal transportation systems in a single dashboard. The experimental solution is applied to the transport network of the city of Porto, Portugal, as a case study. The problem includes such major components as (1) input information and its sources, (2) model (metrics, calculation algorithms) and (3) visualization metaphors. A proposed solution is a dashboard that provides a comprehensive view on the operational state, or Level of Service (LOS), of transportation systems of different types. Such a system is expected to become an essential tool for transport system mon- itoring and management, supplying the necessary information for strategic and emergency planning to the authorities and other decision makers in the sector. address the above issues specifically for the ITSs used in the city of Porto, Portugal. Our aim was first of all to study the possibility of providing a global view on the whole transportation network of any scale and type, in order to monitor its transportation systems at different levels of abstraction and evaluate their operational state based on that of the elements and subsystems they include, as well as the operational state of the transportation network as a whole. In order to achieve this aim, we have developed an hierarchical model of LOS evaluation inspired by the concept of ontology. For testing purpose we have implemented a prototype of the dashboard that combines the LOS measures of different transport modes and detail level and permits LOS analysis at different geographic scale: the entire monitored zone, its areas and sub-areas. To make that possible, it was necessary to identify the protocols and other sources supplying data related to LOS for different transport means (aviation, road, waterborne, railway) in a real-time mode. Besides that, a study has been performed to identify and define the metrics, as well as their scale for the evaluation of LOS of each transport system and respective subsystems. The result of the research that has been carried out and the prototype architecture are described in this paper.

Sensor technology available in smartphones enables the monitoring of mobility patterns, which could be of particular interest for the transportation sector. For example, driving time information can help to determine if a selected path is the most convenient. Moreover, measurements related to the time expended on the road and origin destination matrices can lead to conclusions related to the organization of travel schedules and routes, enhancing reliability and resulting in a shorter total traveling time. Relying on global positioning system (GPS)-based Floating Car Data (FCD), the authors designed a platform to acquire data for the evaluation of traffic conditions and driving performance using mobile phone sensors. Users control the activation of the tracking activity themselves and can benefit from information provided by other users' records. Additional metrics related to the travel time and vehicle's speeds contribute to the assessment of traffic management issues. Conclusions regarding possible applications of the tool are outlined.

Unmanned Aerial Vehicles (UAVs) are gaining the attention of researchers around the world. Recently researchers apply the multi-agent paradigm to such domain taking advantage of its research in cognition, deliberation, and mainly coordination mechanisms. The aim of our research is to contribute to the multiple quadcopter simulation area introducing a platform for the design, simulation, and development of quadcopter MultiAgent Systems (MAS). Specifically for this paper, we minutely discuss the proposed architecture and present the implemented prototype focusing our experiments on the scalability and the resource usage of it. The results demonstrate that the centralised perspective limits the scalability mainly when the physical engine is being used; also, we identify that the method responsible for the 3D update is the platform's bottleneck. After this work different type of studies in social interaction of quadcopters will follow up as well as the improvement of the proposed architecture, for instance, using a multi-threading approach.

Understanding human behaviour in emergency evacuation from buildings is of utmost importance for fire safety designers, architects and engineers as they elaborate on strategies to improve the emergency paths to exits. This paper describes an experiment designed to elicit human behaviour when facing the urgent need of exiting a room of an unknown building. This test is part of a methodological approach that aims at the creation of a framework coined SPEED (Simulation of Pedestrians and Elicitation of their Emergent Dynamics). A population sample of 22 subjects was used to test such a methodological approach, which consists in having the subjects answering a questionnaire and later on, in playing a Serious Game. The game environment presents the same scenarios shown in the questionnaire using more elaborated 3D rendering to provide players with a sense of realism. The game was developed under the Unity3D game engine and based on the Serious Games concept. Preliminary results are promising, showing that the challenge made players think about the various situations that might happen when facing an emergency. They are also implied to reason on their stream of decisions, such as which direction to take considering the environment and some adverse situations, such as smoke, fire and people running on the opposite direction of the emergency signalling.

Small-scale Unmanned Aerial Vehicles (UAVs) are gaining the attention of researchers around the world, due to their versatility and performance in both indoor and outdoor applications. The whole project aims to specify and implement a symbiotic simulation platform to assist the design, development and testing of UAVs that use a quad-rotor configuration. Additionally, in this paper we firstly discuss the integration of two software components to build the simulation environment: Jason, an agent-oriented platform for the development and simulation of multi-agent systems and the jME3 game engine, used for 3-dimensional simulations of the models. Further, we describe the implementation of the physical multi-rotor system and present some preliminary results, as well as the description of the symbiotic simulation platform.

The development of Advanced Driver Assistance Systems (ADAS) is rapidly growing. However, most of the ADAS require field test, which is expensive, unpredictable and time consuming. In this paper we propose a multiagent-based driving simulator which integrates a human factor analysis suite and enables rapid and low-cost experimentation of mobile-device ADAS. Our architecture uses a microscopic simulator and a serious-game-based driving simulator. The latter allows the user to control a vehicle and change the correspondent simulation state in the microscopic simulator. The driving simulator also connects to an Android device and sends several kinds of data, such as current GPS coordinates or transportation network data. One important feature of this architecture is its suitability to serve as an appropriate means to conduct behaviour elicitation through peer-designed agents, so as to improve modelling of various driving styles accounting for different aspects of preferences and perception abilities, as well as other performance measures related to drivers' interaction with ADAS solutions. The potentials of our approach to aid experiments in human factor analysis are still to be tested, but are undoubtedly huge and encouraging.

The dynamics of pedestrians have received less attention in transportation systems when compared to cars and other transportation means. However, its importance is inarguable and, recently, a lot of effort on research was put into this field. The use of Agent-Based Models (ABM) and known techniques such as the Social Forces Model (SFM) has fostered this most recent application field. Several models are already available for crowd simulation, but the validation of such models is a critical issue. Methodologies for behaviour elicitation and validation in social simulation models are needed. In this paper, a new framework to tackle these problems is proposed. Based on research made for more specific applications, concerned with buildings' evacuation, a methodological approach to develop a simulator for pedestrian's dynamics consisting of five main tasks is proposed hereafter. The tasks are: 1) "Reference Models and Benchmarks" 2) "Pedestrian Sensor Fusion, 3) "Serious Games for Pedestrian Behaviour Elicitation" 4) "Behaviour Mining, " and 5) "Agent-Based Modelling and Simulation of Pedestrian Elicitation of Emergent Dynamics, " a Decision Support Tool aimed at the study and analysis at pedestrian dynamics in the urban context, specifically for helping planners to develop the new generation of buildings and cities.

Traffic congestion affects not only the economic activity of cities but is also responsible for air quality and global warming problems. The employment of electric buses in metropolitan transportation as an alternative to internal combustion engine buses appears to be a promising eco-sustainable solution. However, an important aspect in evaluating the performance and adequateness of such vehicles is the fact they are immersed in an urban context. This work follows a HLA-based distributed architecture for electric bus powertrain simulation within a realistic urban mobility environment. In that sense, it discusses on coupling the SUMO (Simulation of Urban MObility) microscopic traffic simulator with a model of an electric bus powertrain subsystem designed in the MatLab/Simulink environment. Both the electric bus traction subsystems and the integration have been validated using field test experimental data. The proposed simulation framework is multi-faceted. As a matter of fact it can be used, not only as electric vehicle evaluation tool under different dynamics of traffic conditions and driving behavior, but also as a planner for charging strategies and optimizations.

Inter-modal interfaces are extremely important for the transportation system as a whole. Therefore, designing and dimensioning airport terminals' curbsides are a major step to improve passengers' experience. Indeed, evaluating such design is a critical task that must be performed to point out the best solution. This paper reports on our modelling, implementation, and evaluation of two curbside scenarios: one as currently applied to the Portela Airport, in Lisbon, and another alternative as proposed by a Transport Consultancy Agency. The contributions are to create a broader methodology to evaluate various scenarios as well as to combine different granularities of analysis in order to leverage a multi-resolution simulation where, under more realistic circumstances, the best scenario can be pointed out. Some preliminary experiments are carried out, whose results are presented and discussed so as to compare their efficiency. Finally, we draw some conclusions and identify ways in which this work can be further extended.

The incorporation of Augmented Reality (AR) in the windshield of automobiles using heads-up displays (HUD) is starting to be implemented by some manufacturers and is proving to be very useful in some situations, including safety distance keeping. This paper reports on a system that warns the driver via the car's HUD when he violates the predefined safety distance, to avoid a possible forward collision, and proposes two different visualization metaphors based on traffic signals. The metaphors are compared, with and without warning sounds, through computer simulations performed by 22 participants from different age groups and driving experiences. One of the metaphors corresponds to a variant of the traffic sign C10 that forbids circulating from the preceding vehicle shorter than a certain minimum headway, whereas the other corresponds to the road safety marks, which recommend the safety distance to be observed from the vehicle ahead. Results show that the metaphor derived from the safety marks, with warning sounds, is preferred by the participants, and was considered the most useful, intuitive and adequate to a forward collision warning. We also found that this metaphor was preferred by all participants that were 42 or more years old, whereas participants between 28 and 41 years old were divided between the two metaphors, with warning sounds.

The execution of an agent's complex activities, comprising sequences of simpler actions, sometimes leads to the clash of conflicting functions that must be optimized. These functions represent satisfaction, short-term as well as long-term objectives, costs and individual preferences. The way that these functions are weighted is usually unknown even to the decision maker. But if we were able to understand the individual motivations and compare such motivations among individuals, then we would be able to actively change the environment so as to increase satisfaction and/or improve performance. In this work, we approach the problem of providing highlevel and intelligible descriptions of the motivations of an agent, based on observations of such an agent during the fulfillment of a series of complex activities (called sequential decisions in our work). A novel algorithm for the analysis of observational records is proposed. We also present a methodology that allows researchers to converge towards a summary description of an agent's behaviors, through the minimization of an error measure between the current description and the observed behaviors. This work was validated using not only a synthetic dataset representing the motivations of a passenger in a public transportation network, but also real taxi drivers' behaviors from their trips in an urban network. Our results show that our method is not only useful, but also performs much better than the previous methods, in terms of accuracy, efficiency and scalability.

With the number of in-vehicle information systems and the complexity of their tasks growing at a very high rate in near future, we need a clear understanding of their related distraction or mental workload and its impact on driver performance. Thus, in this paper we introduce these concepts already in the development phase of a product that will be used in an in-vehicle environment. We present the IC-DEEP (In-Car Ergonomics Evaluation Platform), which is an implementation approach in form of a Serious Game (SG) to autonomously assess, under low-time and low-cost conditions, the factors that can jeopardize the driving performance when manipulating or receiving information from in-vehicle information systems (IVISs). We evaluate the feasibility of the proposed approach and draw conclusions on its effects on behavioral changes and IVIS assessment.

Digital maps are very commonly used in the analysis, administration and representation of urban spaces. Despite the degree of information they can provide, they still pose some difficulties in decision making due to the three-dimensional nature of such ever-changing settings. This paper presents a collaborative solution for large virtual environment recreation aimed at urban landscape simulation. By providing an extensive interactive three-dimensional virtualization of a real-world city, multiple users may contribute with additional data, to either improve the model fidelity or to preview the impact of certain urban changes. This is achieved by employing procedural modeling methods to generate a basic three-dimensional city model from real-world data and a set of parameters, which can later be refined through human intervention. A multi-user simulation platform has been conceived, allowing the collaborative management of the model data, as well as the simulation of possible urban landscape changes, introducing therefore more advanced analysis and representation features to study and discuss the impact of certain decisions on the urban landscape.