Examinando por Autor "Pijoan Lamas, Ander"

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    Agent based simulations for the estimation of sustainability indicators
    (Elsevier B.V., 2015) Pijoan Lamas, Ander; Borges Hernández, Cruz E.; Oribe Garcia, Iraia; Martín Andonegui, Cristina; Alonso Vicario, Ainhoa
    We present a methodology to improve the estimation of several Sustainability Indicators based on the measurement of walking distance to infrastructures combining Agent Based Simulation with Volunteer Geographic Information. Joining these two forces we construct a more realistic and accurate distribution of the infrastructures based on knowledge created by citizens and their perceptions instead of official data sources. A Situated Multi-Agent System is in charge of simulating not only the functional disparity and sociodemographic characteristics of the population but also the geographic reality in a dynamic way. Namely, the system will analyze different geographic barriers for each collective bringing new possibilities to improve the assessment of the needs of the population for a more sustainable development of the city. In this article we will describe the methodology to carry on several sustainability indicator measurements and present the results of the proposed methodology applied to several municipalities.
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    Geoworldsim: A time-asynchronous, distributed and intelligent environment based geosimulation platform
    (Universidad de Deusto, 2018-12-05) Pijoan Lamas, Ander; Alonso Vicario, Ainhoa; Borges Hernández, Cruz E.; Facultad de Ingeniería; Ingeniería para la Sociedad de la Información y Desarrollo Sostenible
    Imagine that the accessibility of the population to public infrastructures needs to be evaluated. A possible solution would be to calculate every distance and analyse which percentage of the population is in less than 300 meters. However, this solution does not take into account issues such as: the age distribution, functional diversity or people’s preferences when transiting the city. It is in these cases when it is necessary to go a step further and integrate Geographic Information Systems with other perspectives such as Multi-Agent Systems which represent the particular characteristics of each individual and their decision making processes. This integration is known as Geosimulation and builds more accurate simulations to model the physical reality together with social, demo graphic and economic components. Geosimulations aim at modelling systems at the scale of individuals and entity-level units of the built environment and provides a way to simulate big amounts of agents interacting in a virtual geographic environment and endowed with spatial cognitive capabilities (perception, navigation, reasoning). This dissertation presents a new Geosimulation platform design and implementation that allows analysing and simulating different urban infrastructures. The platform manages to put into practice the latest theories in Multi-Agent Systems along with the new techniques in cloud computing and asynchronism. The proposed design is evaluated for three case studies; ubiquitous IoT, sustainable transport policies and resilience of the power grid. The methodologies presented, provide progress to their respective research areas by improving state-of-the-art techniques or designing new mechanisms. Furthermore, by connecting these Geosimulations to the real world by sensors and actuators, the concept of mixed reality arises; simulations where changes in the real world are transferred to the virtual world through sensors and agents can influence the real world through actuators. Mixed realities allow developing distributed control systems, which not only take into account the physical reality and social preferences but also the state, where to deploy intelligent agents that provide services to citizens.
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    A human-centric context-aware IoT framework for enhancing energy efficiency in buildings of public use
    (Institute of Electrical and Electronics Engineers Inc., 2018-06-26) Casado Mansilla, Diego ; Moschos, Ioannis; Kamara Esteban, Oihane ; Tsolakis, Apostolos C.; Borges Hernández, Cruz E. ; Krinidis, Stelios ; Irizar Arrieta, Ane; Kitsikoudis, Konstantinos; Pijoan Lamas, Ander; Tzovaras, Dimitrios; López de Ipiña González de Artaza, Diego
    The GreenSoul project introduces an innovative energy-efficient platform which enhances traditional public-use buildings with various technologies' such as smart adaptors' energy analyzers' an occupant aware decision support engine' mobile applications' and interactive ambient interfaces. These enhancement aims to directly improve the interactions between occupants and energy-consuming assets in their environment. The GreenSoul framework is further enriched by the deployment of lightweight edge-computing GreenSoul-ed devices' which reduce energy consumption by cooperating with other devices' smart metering equipment and' very importantly' with eco-aware users. The decision making process is supported by a socio-economic behavioral model' which provides the necessary understanding of occupant indoor behavior toward transforming energy consuming devices into active pro-sustainability agents that inform users how energy-efficiently they operate them' provide notifications as to improve that aspect' and even adapt their own functioning to reduce energy waste. These eco-wise devices' which are coined as GreenSoul-ed Things' are explained in detail throughout this paper as well as the sensor-based architecture that supports their operation. The deployment of the framework across six pilot buildings is addressed' as well as the designed experimental setting to corroborate the potential of such a collaborative approach to enhance energy efficiency in office buildings.
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    Transport choice modeling for the evaluation of new transport policies
    (MDPI, 2018-04-17) Pijoan Lamas, Ander; Kamara Esteban, Oihane; Alonso Vicario, Ainhoa; Borges Hernández, Cruz E.
    Quantifying the impact of the application of sustainable transport policies is essential in order to mitigate effects of greenhouse gas emissions produced by the transport sector. One of the most common approaches used for this purpose is that of traffic modelling and simulation, which consists of emulating the operation of an entire road network. This article presents the results of fitting 8 well known data science methods for transport choice modelling, the area in which more research is needed. The models have been trained with information from Biscay province in Spain in order to match as many of its commuters as possible. Results show that the best models correctly forecast more than 51% of the trips recorded. Finally, the results have been validated with a second data set from the Silesian Voivodeship in Poland, showing that all models indeed maintain their forecasting ability.