As we gradually move towards smarter cities, having greater control of the traffic in the city becomes of utmost importance. In addition, to efficiently manage such traffic, it is critical to be able to predict the impact of different traffic policies, and potential changes to the city road systems structure. To this end, accurate traffic simulation models must be derived that can help in this task. This paper presents a tool that aims to improve the representativeness of traffic simulations by generating realistic Origin-Destination (OD) traffic matrices. In particular, we focus on cities whose source of traffic information are the induction loop detectors deployed through the different streets and avenues of the city. By comparing against the widely used DFROUTER tool, part of the SUMO open-source traffic simulation package, we show how we are able to improve the traffic model accuracy significantly. Specifically, we achieved more realistic route lengths and a better distribution of traffic sources and destinations.

A fundamental problem in the design of a classroom is to identify what characteristics it should have in order to optimize learning. This is a complex problem because learning is a construct related to several cognitive processes. The aim of this study is to maximize learning, represented by the processes of attention, memory, and preference, depending on six classroom parameters: height, width, color hue, color saturation, color temperature, and illuminance. Multi-objective integer linear programming with three objective functions and 56 binary variables was used to solve this optimization problem. Virtual reality tools were used to gather the data; novel software was used to create variations of virtual classrooms for a sample of 112 students. Using an interactive method, more than 4700 integer linear programming problems were optimally solved to obtain 13 efficient solutions to the multi-objective problem, which allowed the decision maker to analyze all the information and make a final choice. The results showed that achieving the best cognitive processing performance involves using different classroom configurations. The use of a multi-objective interactive approach is interesting because in human behavioral studies, it is important to consider the judgement of an expert in order to make decisions.

Green parks are the only natural places for recreation in many metropolitan areas, and the European Commission is seeking to improve their air quality and, consequently, citizens’ physical and mental health. One of the recently adopted approaches is to achieve pollution abatement in these green areas by reducing nearby traffic. In this paper, we analyze the impact of reducing the traffic in nearby streets to avoid pollution by proposing two different approaches. Our goal is to improve the pollution levels in Valencia’s most significant green areas by limiting vehicular traffic flow in nearby streets. To this end, we consider two alternative solutions—a more restrictive one and a less restrictive approach—in an attempt to achieve a tradeoff between emission control and congestion avoidance. Moreover, we show how our proposal can reroute traffic throughout the city without having traffic jam problems associated with the proposed approaches. In addition, we determine how the traffic flow data and the emissions in the city vary due to the traffic restrictions that we enforce. The experimental results show that it is possible to achieve improvements in terms of pollution with both of our restriction approaches; in particular, with the partial traffic isolation model, the pollution rates in the target area decreased by 17%, which we consider an excellent initial result for the applicability and effectiveness of these methods when an adequate traffic routing system is adopted.

The population pyramids in Europe have changed in the last decades, particularly in Spain, where population aging is observed and implies an increase in the dependency ratio. The present study aims to modify a mathematical model of population defined by age and sex based on the von Foerster-Mckendrick equations in order to have birth and migration rates as control variables. To achieve this objective, the necessary mathematical changes are made in the model, and the new mathematical model is verified and validated for the case of Spain, in the period 2008–2019, being considered successful both in its deterministic and stochastic formulation. Finally, through the method of strategies and scenarios and a genetic algorithm, a modification of the population pyramid is obtained in the direction of a decrease in the dependency ratio, increasing the birth rate and immigration and decreasing the emigration rate. These changes lead to a modification of the population pyramid in the year 2040; in particular, an increase of the female population in the age range from 20 to 39 years old.

This article presents the work carried out to adapt the subject Mathematics II in the Technical Architecture degree of the Polytechnic University of Valencia (UPV) to the confinement conditions due to the COVID-19 pandemic during the second semester of the 2019-20 academic year. Some conclusions and mechanisms used during this period that are considered useful in nonpandemic conditions are also presented.

En este artículo se expone el trabajo realizado para adaptar la asignatura Matemáticas II en el grado de Arquitectura Técnica de la Universitat Politècnica de València (UPV) a las condiciones de confinamiento por la pandemia de la COVID-19 durante el segundo cuatrimestre del curso 2019-20. Se presentan también algunas conclusiones y algunos mecanismos utilizados durante este período que se consideran útiles en condiciones sin pandemia.

Buildings account 40% of the EU's total energy consumption. Therefore, they represent a key potential source of energy savings to fight, among others, against climate change. Furthermore, around 54% of the buildings in Spain date back before 1980, when no thermal regulation was available. The refurbishment of a façade of an old building is usually the most effective way to improve its energy efficiency, by adding layers to the external envelope in order to reduce its thermal transmittance.This paper deals with the problem of minimizing costs for the thermal refurbishment of a façade with thickness and thermal transmittance bounds and with an intervention both on the opaque part (wall) and the transparent part (windows). Among thousands, even millions of combinations of materials and thicknesses for the different layers to be added to the opaque part, types of frame, and combinations of glasses and air chambers for the transparent part, the aim is to choose the one that minimizes the cost without violating any restriction imposed to the thermal refurbishment, in particular the current energy efficiency regulations in the zone.To optimally solve this problem, it will be modelled as an Integer Linear Programming problem with binary variables. The case study will be Building 1B of the School for Building Engineering of the Polytechnic University of Valencia, Spain. It was built in the late 1960s and has had a very inefficient energy consumption record. The optimal solution will be found among more than 6 million feasible solutions.

There are few studies developed about the general factor of personality (GFP) dynamics. This paper uses a dynamical mathematical model, the response model, to predict the short‐term effects of a dose of alcohol on GFP and reports the results of an alcohol intake experiment. The GFP dynamical mechanism of change is based on the unique trait personality theory (UTPT). This theory proposes the existence of GFP, which occupies the apex of the hierarchy of personality. An experiment with 37 volunteers was performed. All the participants completed The five‐adjective scale of the general factor of personality (GFP‐FAS) in trait‐format (GFP‐T) and state‐format (GFP‐S) before alcohol consumption. The participants in the experimental group (28) received 26.51 g of alcohol and a slight food, while the participants in the control group (9) just received the food. Every participant filled the GFP‐S each 7 minutes. The results show that GFP is modified by a single dose of alcohol: both the high scores of GFP‐T and the high scores of GFP‐S explain the most part of the alcohol impact. Moreover, they prove that the response model calibration to the GFP‐S scores reproduces the biphasic GFP dynamics as a consequence of an alcohol dose intake described by the literature. In fact, the results also demonstrate that the response model provides the UTPT prediction: the high scores of GFP‐T predict a stronger stimulant‐like effect and a stronger inhibitor effect. Thus, the response model is a useful mathematical tool to predict those individuals inclined to the alcohol misuse.

Buildings are responsible for about 36% of the CO2 emissions in Europe but there is a significant potential to reduce these emissions. This paper deals with the embodied CO2 emissions of the opaque part of a facade, which include the life cycle of any material used in its construction: excavation, processing, construction, operation, maintenance, demolition and waste or recycling. With the aim of minimizing such embodied CO2 emissions, an Integer Linear Programming problem is presented, in which CO2 emissions are minimized depending on other parameters involved in the construction of the facade, like the maximal thermal transmittance allowed by current legislation, thickness of the wall, budget, availability of materials for the different layers of the wall, etc. The paper also shows a case study based on a constructive solution for the opaque part of the envelope defined by up to six layers, with more than 1.1 million possible combinations. This case study considers seventy scenarios depending on maximal allowed thermal transmittances and thickness intervals for five different technologies applied to the structural element of the wall. Results show that an adequate selection of materials can reduce the embodied CO2 emissions of the opaque part of the envelope up to 78.5% for similar values of transmittance and thickness.

A system methodology for modeling and optimizing social systems is presented. It allows constructing dynamical models formulated stochastically, i.e., their results are given by confidence intervals. The models provide optimal intervention ways to reach the stated objectives. Two optimization methods are used: (1) to test strategies and scenarios and (2) to optimize with a genetic algorithm. The application case presented is a small nonformal education Spanish business. First, the model is validated in the 2008-2012 period, and subsequently, the optimal way to obtain a maximum profit in the 2013-2025 period is obtained using the two methods.

Currently, one of the main challenges that large metropolitan areas must face is traffic congestion. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, environmental pollution. By properly analyzing traffic demand, it is possible to predict future traffic conditions, using this information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution, thereby improving the traffic flow in a city in a fully centralized manner. This paper represents a step forward towards this novel traffic management paradigm by proposing a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. We perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed traffic prediction equation, combined with frequent updating of traffic conditions on the route server, can achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.

Currently, one of the main challenges that large metropolitan areas have to face is traffic congestion. To address this problem it is necessary to implement an efficient solution to control traffic that generates benefits for citizens such as reducing vehicle journey times and, consequently, environmental pollution as well. By properly analyzing traffic demand it becomes possible to predict future traffic conditions, and use this information for the optimization of routes. Such an approach becomes especially effective if applied in the context of automated vehicles, which have a more predictable behavior, thus being capable of mitigating the effects of traffic congestion by improving the traffic flow of the city in a centralized manner. This paper performs an experimental study of traffic congestion in an area characterized by intense traffic in the city of Valencia, Spain. By comparing the traffic flow in a typical day with our proposed improved solution, we show that significant benefits are achieved. In particular, we have created an interface to connect an existing urban traffic simulator (SUMO), and a network simulator (OMNeT++), with a modified route server. The latter continually updates present and future traffic conditions to properly balance traffic throughout the city. Experimental results show that our proposed Traffic Prediction Equation, combined with frequent updating of traffic conditions on the route server, is able to achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.

The construction of a buildings external wall is subject to many restrictions such as budget, workforce, availability of materials, thickness, maintenance cost, time limit and specially, energy efficiency legislation intended at mitigating the negative effects of the energy consumption and obtaining a more sustainable and healthier indoor environment. The choice of the appropriate material and thickness composing each layer of an external wall can significantly reduce the energy consumption of the building without adversely affecting the cost of the wall.By using Integer Linear Programming (ILP), the aim of this paper is to obtain this best choice of materials and thicknesses to minimize the construction cost of an external wall while complying with the abovementioned restrictions. A case study is presented with more than 5.5 million combinations of different selected materials and their thicknesses for the different layers of the wall. The ILP problem has been solved for 165 scenarios that take into account different maximal allowed thermal transmittances and a range of the most usual thicknesses and material options of an external wall.

Currently, one of the main challenges faced in large metropolitan areas is traffic congestion. To address this problem, adequate traffic control could produce many benefits, including reduced pollutant emissions and reduced travel times. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. This paper performs an experimental study of the traffic distribution in the city of Valencia, Spain, characterizing the different streets of the city in terms of vehicle load with respect to the travel time during rush hour traffic conditions. Experimental results based on realistic vehicular traffic traces from the city of Valencia show that only some street segments fall under the general theory of vehicular flow, offering a good fit using quadratic regression, while a great number of street segments fall under other categories. Although in some cases such discrepancies are related to lack of traffic, injecting additional vehicles shows that significant mismatches still persist. Thus, in this paper we propose an equation to characterize travel times over a segment belonging to the sigmoid family; specifically, we apply logistic regression, being able to significantly improve the curve fitting results for most of the street segments under analysis. Based on our regression results, we performed a clustering analysis of the different street segments, showing that they can be classified into three well-defined categories, which evidences a predictable traffic distribution using the logistic regression throughout the city during rush hours, and allows optimizing the traffic for automated vehicles.

This paper presents a new human happiness index built through five dimensions: development, freedom, solidarity, justice and peace. These five dimensions are evaluated through quantitative variables obtained from the Human Development Reports, World Data Bank and Eurostat. The new happiness index has been built following the guidelines set by the Human Development Reports of the UN for the construction of quality indices, and it has been compared on a set of 13 EU countries with the Overall Life Satisfaction Index, which is used by the UN . Moreover, the new index has been included in a dynamic mathematical model through the demographic rates to study the evolution of the population. The obtained model has been calibrated for the period 2004–2009 and validated for the period 2010–2015 for the case of Spain. Finally, the model has been used to maximize the happiness index in Spain for the period 2016–2030, with the conclusion that to achieve this purpose, it is necessary to invest in education, research and development.

This paper deals with the minimization of a building's external wall thermal transmittance, with the aim of improving the energy efficiency of the building. The wall's thermal transmittance must abide by the current legislation, but also suit the limitations of other construction parameters, mainly budget and thickness, but also time limit, workforce, number and thickness of the layers and availability of materials depending on the approach. The optimization is achieved formulating an Integer Linear Programming (ILP) problem involving the parameters mentioned above. Therefore, any available ILP solver can be run to obtain the best combination of the different materials and thicknesses for the layers, in order to minimize the thermal transmittance. This paper presents a case study of a common but representative external wall consisting of 6 layers, with more than 670,000 possible combinations of materials and their thicknesses. The study concludes with a comparison of the lowest thermal transmittance obtained for a selection of budget and thickness combinations for the mentioned wall. (C) 2017 Elsevier B.V. All rights reserved.

This paper presents a stochastic dynamic mathematical model to study the evolution of the unemployment rate and other relevant related variables in a country. This model is composed by three basic interrelated subsystems: demographic, economic and well-being ones. A key aspect of this model is that it considers three UN well-being variables simultaneously: Human Development Index, Gender Empowerment Index and Gender Differentiation Index. These variables involve key concepts for human development, as Health, Education, Economy and Female Labor.With this model, the most outstanding variables found in the literature in relation with unemployment control can be used to design strategies and scenarios to reduce the unemployment rate in the future. The model has been fitted for the case of Spain in the 2002–2014 period, the largest one with information about all the variables involved in the model. Finally, several tentative scenarios and strategies have been tested to reduce the unemployment rate in Spain in the horizon of year 2025, and the corresponding forecast evolution of the Gender Differentiation Index, National income per capita, Public debt and the ratio between Public debt and Gross domestic product are shown.

Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels.

Nowadays, one of the main challenges faced in large metropolitan areas is traffic congestion. To address this problem, an adequate traffic control could produce many benefits, including reduced pollutant emissions and reduced travel times. If it were possible to characterize the state of traffic by predicting traffic conditions, measures could be taken to preventively mitigate the effects of congestion and related problems. This paper performs an experimental study of the traffic distribution in the city of Valencia, characterizing the different streets of the city in terms of vehicle load with respect to the travel time during rush hour traffic conditions. Experimental results based on realistic vehicular traffic traces show that most of the street segments under analysis present a good fit under quadratic regression, although a large number of street segments fall under other categories mainly due to lack of traffic. Based on this study, a clustering analysis study associated to the different streets shows how these streets can be classified into four independent categories, evidencing an uneven traffic distribution throughout the city.

The high levels of urban traffic is becoming a main concern in our societies, generating problems such as excessive fuel consumption, CO 2 emissions. Recently, Intelligent Trans-portation Systems (ITS) have emerged as a way to mitigate these problems. However, traffic analysis, improvement typically rely on simulations, which should be as realistic as possible. Meeting this requirement can be complex when the actual traffic patterns, defined through an origin/destination (O-D) matrix for the vehicles in a city, remain unknown. In this paper we propose a novel technique in order to import traffic data to the SUMO mobility simulation tool. In particular, our approach starts from induction loop measurements available from traffic authorities, and then it uses the DFROUTER tool, along with a heuristic, to generate an O-D matrix for traffic that resembles the real traffic distribution. We apply our technique to the city of Valencia, and the obtained results are then compared through simulation to other existing traffic mobility data for the cities of Cologne (Germany), Bologna (Italy), demonstrating the validity of our approach.