Antonio David Masegosa

Universtiy of Deusto/ Ikerbasque - Basque Foundation for Science

Intelligent Transportation Systems are producing tons of hardly manageable traffic data, which motivates the use of Machine Learning (ML) for data-driven applications, such as Traffic Forecasting (TF). TF is gaining relevance due to its ability to mitigate traffic congestion by forecasting future traffic states. However, TF poses one big challenge...

Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a...

The growth in e-commerce that our society has faced in recent years is changing the view companies have on last-mile logistics, due to its increasing impact on the whole supply chain. New technologies are raising users’ expectations with the need to develop customized delivery experiences; moreover, increasing pressure on supply chains has also cre...

Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which...

In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intrica...

The progressive electrification of urban distribution fleets, motivated by the consolidation of electric vehicle technology and by the mobility advantages that cities grant to non-polluting vehicles, poses future challenges that affect electrical distribution networks. This paper simulates the main last mile distribution models that can be adopted...

The maximal covering location problem attempts to locate a limited number of facilities in order to maximize the coverage over a set of demand nodes. This problem is NP-Hard and it has been often addressed by using metaheuristics, where the execution time directly depends on the number of evaluations of the objective function. In this paper, the pr...

In the era of data-driven decision making, the under-utilization of available data sources prevents organizations and corporations from unlocking their full potential and might even threaten their existence. On a city level, public authorities typically have access to numerous heterogeneous data sources, which are either generated by proprietary in...

Induced Travel Demand is a phenomenon (ITD) wherein building new road infrastructure increases private car use. ITD has been measured and corroborated by means of econometric models that give an account of how much travel demand can be induced after road construction, without claims of causality in their inner structure (black-box approach). Howeve...

Currently, there are no guidelines to determine what are the most suitable machine learning pipelines (i.e. the workflow from data preprocessing to model selection and validation) to approach Traffic Forecasting (TF) problems. Although automated machine learning (AutoML) has proved to be successful dealing with the model selection problem in other...

Traffic forecasting is an important research area in Intelligent Transportation Systems that is focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep neural network that simultaneously extracts the spatial features of traffic, using graph convolution, and its temporal features by means of Long Short Term Me...

Traffic forecasting is a well-known strategy that supports road users and decision-makers to plan their movements on the roads and to improve the management of traffic, respectively. Current data availability and growing computational capacities have increased the use of machine learning methods to tackle traffic forecasting, which is mostly modell...

The articles in this special section focus on data driven optimization for transportation and smart mobility applications. We live in an era of major societal and technological changes. Transportation de-carbonization and postindustrial demographic trends, such as massive migrations and an aging society, generate new challenges for cities, making t...

Within the framework of the work carried out by the University of Deusto (http://www.deusto.es/) on the social impact of research, a series of research projects with high potential for social impact are selected annually, and from these, the so-called Deusto Social Impact Briefings (DSIB) are prepared and published as short monographs. They are aim...

Within the framework of the work carried out by the University of Deusto (http://www.deusto.es/) on the social impact of research, a series of research projects with high potential for social impact are selected annually, and from these, the so-called Deusto Social Impact Briefings (DSIB) are prepared and published as short monographs. They are aim...

One of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheurist...

Connected vehicles are revolutionizing the way in which transport and mobility are conceived. The main technology behind is the so-called Vehicular Ad-Hoc Networks (VANETs), which are communication networks that connect vehicles and facilitate various services. Usually these services require a centralized architecture where the main server collects...

One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computational capacities have increased the use of Machine Learning to address traffic prediction, which is mo...

One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computational capacities have increased the use of Machine Learning to address traffic prediction, which is mo...

Many services and commodities have been deployed in airport terminals to provide additional conveniences. About half of revenues of airports in Europe come from the on-site non-aeronautical services offered. For this reason, the optimization of the existing resources is critical to maximize the profitability of the rented spaces. The main objective...

Despite the broad range of Machine Learning (ML) algorithms, there are no clear baselines to find the best method and its configuration given a Short-Term Traffic Forecasting (STTF) problem. In ML, this is known as the Model Selection Problem (MSP). Although Automatic Algorithm Selection (AAS) has proved success dealing with MSP in other areas, it...

Vehicular Ad-Hoc Networks (VANETs) have attracted a high interest in recent years due to the huge number of innovative applications that they can enable. Some of these applications can have a high impact on reducing Greenhouse Gas emissions produced by vehicles, especially those related to traffic management and driver assistance. Many of these ser...

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of m...

Intelligent Transportation Systems emerged to meet the increasing demand for more efficient, reliable and safer transportation systems. These systems combine electronic, communication and information technologies with traffic engineering to respond to the former challenges. The benefits of Intelligent Transportation Systems have been extensively pr...

A real-world newspaper distribution problem with recycling policy is tackled in this work. To meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries,...

Researchers who investigate in any area related to computational algorithms (both defining new algorithms or improving existing ones) usually find large difficulties to test their work. Comparisons among different researches in this field are often a hard task, due to the ambiguity or lack of detail in the presentation of the work and its results....

The location of facilities (antennas, ambulances, police patrols, etc) has been widely studied in the literature. The maximal covering location problem aims at locating the facilities in such positions that maximizes certain notion of coverage. In the dynamic or multi-period version of the problem, it is assumed that the nodes’ demand changes with...

When we face an optimization problem whose definition (in some aspect) changes over the time, we are in the presence of a Dynamic Optimization Problem (DOP). The aspects that can change are the objective function, the variables' domain, the appearance/disappearance of variables or constraints, etc. This paper aims at providing a first introduction...

Accurate estimation of the future state of the traffic is an attracting area for researchers in the field of Intelligent Transportation Systems (ITS). This kind of predictions can lead to traffic managers and drivers to act in consequence, reducing the economic and social impact of a possible congestion. Due to the inter-urban traffic information n...

In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have b...

In this paper, the literature associated with the covering location problems addressing uncertainty under a fuzzy approach is reviewed. Specifically, the papers related to the most commonly applied models such as set covering location problem, maximal covering location problem, and hub covering location problem are examined. An annotated bibliograp...

Location based services can improve the quality of patient care and increase the efficiency of the healthcare systems. Among the different technologies that provide indoor positioning, inertial sensors based pedestrian dead-reckoning (PDR) is one of the more cost-effective solutions, but its performance is limited by drift problems. Regarding the h...

TIMON is an EU research project under the programme Horizon 2020 that aims at creating a cooperative ecosystem integrating traffic information, transport management, ubiquitous data and system self-management. The objective of TIMON is to provide real-time services through a web based platform and a mobile APP for drivers, Vulnerable Road Users (VR...

Metaheuristics have proven to get a good performance solving difficult optimization problems in practice. Despite its success, metaheuristics still suffers from several problems that remains open as the variability of their performance depending on the problem or instance being solved. One of the approaches to deal with these problems is the hybrid...

This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min....

Dynamic Optimization Problems (DOPs) have attracted a growing interest in recent years. This interest is mainly due to two reasons: their closeness to practical real conditions and their high complexity. The majority of the approaches proposed so far to solve DOPs are population-based methods, because it is usually believed that their higher divers...

This paper studies how the accuracy of the step detection algorithm of a pedestrian dead- reckoning (PDR) system is affected by the sampling frequency and the filtering of the data gathered from a wrist-worn inertial measurement unit (IMU). On the one hand, results show that sensors sampling rate can be reduced and a similar accuracy can still be o...

In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions w...

Since their first appearance in 1997 in the prestigious journal Science, algorithm portfolios have become a popular approach to solve static problems. Nevertheless and despite that success, they have not received much attention in Dynamic Optimization Problems (DOPs). In this work, we aim at showing these methods as a powerful tool to solve combina...

The effectiveness of Intelligent Transportation Systems depends largely on the ability to integrate information from diverse sources and the suitability of this information for the specific user. This paper describes a new approach for the management and exchange of this information, related to multimodal transportation. A novel software architectu...

The growth of Location Based Services and Location Aware Services in indoor environments has focused the attention of the research community on indoor location systems, especially on those based on WLAN networks and Received Signal Strength (RSS). Despite the advances reached, the development of reliable, accurate and low-cost indoor location syste...

In the optimization field there are a number of problems called NP, which are those that can be solved by a nondeterministic polynomial time algorithm for a resolution. Because the real world is not static, but dynamic, the need to bring these problems to test reality is created, hence arise dynamic optimization problems (PODs). One of the classic...

Scenario Planning helps explore how the possible futures may look like and establishing plans to deal with them, something essential for any company, institution or country that wants to be competitive in this globalize world. In this context, Cross Impact Analysis is one of the most used methods to study the possible futures or scenarios by identi...

The best performing methods for Dynamic Optimization Problems (DOPs) are usually based on a set of agents that can have different complexity (like solutions in Evolutionary Algorithms, particles in Particle Swarm Optimization, or metaheuristics in hybrid cooperative strategies). While methods based on low-complexity agents are widely applied in DOP...

An open question that arises in the design of adaptive schemes for Dynamic Optimization Problems consists on deciding what to do with the knowledge acquired once a change in the environment is detected: forget it or use it in subsequent changes? In this work, the knowledge is associated with the selection probability of two local search operators i...

Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation. This book is a...

Companies that want to be competitive must make use of good practices to anticipate the future by analyzing the possible effects of today's decisions on their own long-term development. Scenario planning is among the most extended approaches to accomplish this. One of the techniques often used in scenario planning is Morphological Analysis, which a...

The necessity of developing high-performance resolution methods for continuous optimisation problems has given rise to the emergence of cooperative strategies which combine different self-contained metaheuristics that exchange information among them. However, the majority of the proposals found in the literature make use of population-based algorit...

This work presents a study on the performance of several algorithms on different continuous dynamic optimization problems. Eight algorithms have been used: SORIGA (an Evolutionary Algorithm), an agents-based algorithm, the mQSO (a widely used multi-population PSO) as well as three heuristic-rule-based variations of it, and two trajectory-based coop...

One of the methodologies more used to accomplish prospec-tive analysis is the scenario method. The first stage of this method is the so called structural analysis and aims to determine the most important variables of a system. Despite being widely used, structural analysis still presents some shortcomings, mainly due to the vagueness of the informa...

Technology foresight deals with the necessity of anticipating the future to better adapt to new situations regarding innovations that directly affect business world. One widely spread methodology in technology foresight is Godet's Scenario Method, which includes a module (MICMAC) performing the so-called structural analysis. The goal of the structu...

Developing predictive models is one of the key issues in Systems Biology. A critical problems that arises when these models are built is the parameter estimation. The calibration of these nonlinear dynamic models is stated as a nonlinear programming problems (NLP) and its resolution is usually complex due to the frequent ill-conditioning and multim...

Optimization in dynamic environments is a very active and important area which tackles problems that change with time (as most real-world problems do). In this paper we present a new centralized cooperative strategy based on trajectory methods (tabu search) for solving Dynamic Optimization Problems (DOPs). Two additional methods are included for co...

Having in mind the idea that the computational effort and knowledge gained while solving a problem’s instance should be used to solve other ones, we present a new strategy that allows to take advantage of both aspects. The strategy is based on a set of operators and a basic learning process that is fed up with the information obtained while solving...

Optimization problems are ubiquitous in our daily lives and one way to cope with them is using cooperative optimization systems that allow to obtain good enough, fast enough, and cheap enough solutions. From a practical point of view, the design and the analysis of such systems are complex tasks. In this work, an integrated system (DACOS) for helpi...

Optimisation in dynamic environments is a very active and important area which tackles problems that change with time (as most real-world problems do). The possibility to use a new centralised cooperative strategy based on trajectory methods (tabu search) for solving Dynamic Optimisation Problems (DOPs) was previously introduced showing good result...

In this work we discuss to what extent and in what contexts the use of knowledge discovery techniques can improve the performance of cooperative strategies for optimization. The study is approached over two different cases study that differs in terms of the definition of the initial cooperative strategy, the problem chosen as test bed (Uncapacitate...

Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial. Leída el 6 de abril de 2010

Cooperative strategies are search techniques composed by a set of individual methods (solvers) that, through information exchange, cooperate to solve an optimization problem. In this paper, we focus on the composition of such set and we analyze the results of a cooperative strategy when the solvers in the set are equal (homogeneous) or different (h...

Optimization-based decision support systems (DSSs) are an interesting and important area among the many classes of decision support systems. This paper presents SiGMA, a generic core to build Optimization-based DSSs that tries to be as generic as possible on the on-line addition and use of solvers while preserving the maximum functionality on the A...