DeustoTeka Examinando por Autor "Masegosa Arredondo, Antonio David"

Examinando por Autor "Masegosa Arredondo, Antonio David"

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An adaptive local search with prioritized tracking for Dynamic Environments
(Springer Science and Business Media B.V., 2015-12-01) Masegosa Arredondo, Antonio David; Onieva Caracuel, Enrique; López García, Pedro; Osaba, Eneko; Perallos Ruiz, Asier
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 diversity allows a better detection and tracking of changes. However, recent studies have shown that trajectory-based methods can also provide competitive results. This work is focused on this last type of algorithms. Concretely, it proposes a new adaptive local search for continuous DOPs that incorporates a memory archive. The main novelties of the proposal are two-fold: the prioritized tracking, a method to determine which solutions in the memory archive should be tracked first; and an adaptive mechanism to control the minimum step-length or precision of the search. The experimentation done over the Moving Peaks Problem (MPB) shows the benefits of the prioritized tracking and the adaptive precision mechanism. Furthermore, our proposal obtains competitive results with respect to state-of-the-art algorithms for the MPB, both in terms of performance and tracking ability.
Algorithm portfolio based scheme for dynamic optimization problems
(Taylor and Francis Ltd., 2015-08-01) Fajardo Calderín, Jenny; Masegosa Arredondo, Antonio David; Pelta Mochcovsky, David Alejandro
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 combinatorial DOPs. To this end, we propose a new algorithm portfolio for this type of problems that incorporates a learning scheme to select, among the metaheuristics that compose it, the most appropriate solver or solvers for each problem, configuration and search stage. This method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad, Deceptive and Knapsack) and compared versus two reference algorithms for these problems (Adaptive Hill Climbing Memetic Algorithm and Self Organized Random Immigrants Genetic Algorithm). The results showed the importance of a good design of the learning scheme, the superiority of the algorithm portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its performance versus the reference algorithms.
Assessment of the impact of a fully electrified postal fleet for urban freight transportation
(Elsevier Ltd, 2021-02-24) Martínez Velázquez, Miguel; Moreno Emborujo, Asier; Angulo Martínez, Ignacio; Mateo Domingo, Carlos; Masegosa Arredondo, Antonio David; Perallos Ruiz, Asier; Frías Marín, Pablo
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 in a mega-city such as Madrid. In particular, the impact of carrying out the full load of the last mile distribution by means of electric vehicles is analyzed. Two fundamental aspects are studied, the efficiency of the different routes developed by each transport vehicle and the impact that these routes have in the electrical distribution network. For this purpose, an intelligent route planner, capable of optimizing the distribution of the load among the number of vehicles available in each postal service hub (PSH), is combined with a Reference Network Model that designs and expands the distribution network to supply consumers and electric vehicles. Several scenarios in terms of location and segmentation of postal service hubs are analyzed. From this analysis, it is concluded that reinforcements on the distribution network are avoided if the operation is decentralized (using fourteen PSHs), since a centralized operation (a single PSH) would require longer routes with higher energy consumption. Moreover, decentralized operation would enhance the emissions reduction achieved by electrifying the fleet, since the estimated absolute emissions of the electrified fleet for a decentralized scenario are up to 50% lower compared to a centralized one. Finally, the results reveal that smart charging strategies also contribute to lessen the incremental costs in the distribution network, in addition to significantly reducing the cost of energy supply.
A comparative study on the performance of evolutionary fuzzy and crisp rule based classification methods in congestion prediction
(Elsevier B.V., 2016) Onieva Caracuel, Enrique; López García, Pedro ; Masegosa Arredondo, Antonio David ; Osaba, Eneko ; Perallos Ruiz, Asier
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 nature, the task of predicting the future state of the traffic requires, in most cases, a non-linear patterns search in the input data. In recent years, a wide variety of models has been used to solve this problem in the most accurate way. Due to that, models generated to provide information about the future state of the road are, usually, incomprehensible to a human operator, making impossible to give him/her an explanation about the causes of the prediction. Given the capacity of rule based systems to explain the reasoning followed to classify a new pattern, the advantages and disadvantages of such approaches are explored in this work. To conduct such task, datasets recorded from the California Department of Transportation are created. A 9-kilometer section of the I5 highway of Sacramento is used for this research. Two different types of datasets are built for the experimentation. One of them contains the entire information recorded. The other one contains with a simplified version of the information, considering only the first, middle and last monitored points of the road. Twelve prediction horizons, from 5 to 60 minutes, were considered for prediction. An experimental comparative study involving 16 state of the art techniques is performed. Techniques tested include those that fall within the categories of Evolutionary Crisp Rule Learning (ECRL) and Evolutionary Fuzzy Rule Learning (EFRL). These methods were selected since they offer to the final user, not only a prediction, but also a legible model about the way in which the decision was taken. Techniques are compared in terms of accuracy and complexity of the models generated.
Emerging trends in machine learning assisted optimization techniques across intelligent transportation systems
(Institute of Electrical and Electronics Engineers Inc., 2024) Itoro Afolayan, Blessing; Ghosh, Arka; Fajardo Calderín, Jenny; Masegosa Arredondo, Antonio David
Artificial intelligence (AI) plays a critical role in Intelligent Transport Systems (ITS) as urban areas grow by processing data for safety enhancements, predictive analysis, and traffic management. This results in better traffic control, lower emissions, and preventative actions to lessen the effects of accidents. Despite these developments, there isn't a thorough academic analysis that covers a variety of optimization strategies for transportation AI models. By presenting an in-depth analysis of AI optimization methods and their uses in ITSs, this work seeks to close this knowledge gap and give academics important new information on possible directions for future research. Model-based optimization approaches, reinforcement learning techniques, model-predictive control techniques, and generative AI techniques are the four areas into which this study divides AI optimization techniques for the sake of structure, clarity, and comparative analysis. Subcategories of optimization techniques and their corresponding applications are explored, and each category is thoroughly addressed. Researchers will be better able to comprehend the state of AI optimization for transportation management today and in the future thanks to this methodical methodology. The most cutting-edge optimization methods created in the last five years are summarized in this review. This work acts as a compass for future research initiatives targeted at developing scalable and adaptable AI solutions for transportation management by identifying common approaches and highlighting research needs.
Fleet and traffic management systems for conducting future cooperative mobility.
(Springer, 2026) Papa, Gregor; Vukašinović, Vida; Sánchez Cauce, Raquel ; Cantú Ros, Olivia G.; Burrieza Galán, Javier ; Tympakianaki, Athina ; Pellicer-Pous, Antonio; Masegosa Arredondo, Antonio David ; Gosh, Arka; Serrano, Leire
As urbanization continues to increase worldwide, cities face the challenge of accommodating growing populations while maintaining efficient and sustainable transportation systems. The advent of connected and autonomous vehicles promises transformative changes in urban mobility. This paper addresses developments and innovations aimed at seamlessly integrating CAVs into the complex urban mobility ecosystem. It presents assumptions related to a fleet of fully connected and autonomous vehicles coordinated by traffic management centers and focuses on optimizing route assignments based on various performance metrics, including travel time, energy consumption, congestion, and emissions. We are also exploring the integration of people and goods mobility by leveraging the cost efficiency and versatility of on-demand autonomous services.
Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems
(Elsevier B.V., 2018-01-03) Osaba, Eneko ; Carballedo Morillo, Roberto ; Díaz Martín, José Fernando ; Onieva Caracuel, Enrique ; Masegosa Arredondo, Antonio David ; Perallos Ruiz, Asier
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. On many occasions, the replication of the work conducted by other researchers is required, which leads to a waste of time and a delay in the research advances. The authors of this study propose a procedure to introduce new techniques and their results in the field of routing problems. In this paper, this procedure is detailed, and a set of good practices to follow are deeply described. It is noteworthy that this procedure can be applied to any combinatorial optimization problem. Anyway, the literature of this study is focused on routing problems. This field has been chosen because of its importance in real world, and its relevance in the actual literature.
A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
(Elsevier Ltd, 2020-03) Bogaerts, Toon; Masegosa Arredondo, Antonio David; Angarita Zapata, Juan S.; Onieva Caracuel, Enrique; Hellinckx, Peter
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 Memory (LSTM) cells to make both short-term and long-term predictions. The model is trained and tested using sparse trajectory (GPS) data coming from the ride-hailing service of DiDi in the cities of Xi'an and Chengdu in China. Besides, presenting the deep neural network, we also propose a data-reduction technique based on temporal correlation to select the most relevant road links to be used as input. Combining the suggested approaches, our model obtains better results compared to high-performance algorithms for traffic forecasting, such as LSTM or the algorithms presented in the TRANSFOR19 forecasting competition. The model is capable of maintaining its performance over different time-horizons from 5 min to up to 4 h with multi-step predictions.
A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem
(Elsevier Ltd, 2024-10-15) Rodríguez Esparza, Erick; Masegosa Arredondo, Antonio David; Oliva, Diego; Onieva Caracuel, Enrique
Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming due to 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 hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASARL) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The implementation of the HH improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.
A novel software architecture for the provision of context-aware semantic transport information
(MDPI AG, 2015-05-26) Moreno Emborujo, Asier; Perallos Ruiz, Asier; López de Ipiña González de Artaza, Diego; Onieva Caracuel, Enrique; Salaberria Larrauri, Itziar; Masegosa Arredondo, Antonio David
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 architecture is presented, with particular emphasis on the design of the data model and the enablement of services for information retrieval, thereby obtaining a semantic model for the representation of transport information. The publication of transport data as semantic information is established through the development of a Multimodal Transport Ontology (MTO) and the design of a distributed architecture allowing dynamic integration of transport data. The advantages afforded by the proposed system due to the use of Linked Open Data and a distributed architecture are stated, comparing it with other existing solutions. The adequacy of the information generated in regard to the specific user’s context is also addressed. Finally, a working solution of a semantic trip planner using actual transport data and running on the proposed architecture is presented, as a demonstration and validation of the system.
Optimization and machine learning applied to last-mile logistics: a review
(MDPI, 2022-04-28) Giuffrida, Nadia ; Fajardo Calderín, Jenny; Masegosa Arredondo, Antonio David; Werner, Frank ; Steudter, Margarete ; Pilla, Francesco
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 created additional challenges for suppliers. At the same time, this phenomenon generates an increase in the impact on the liveability of our cities, due to traffic congestion, the occupation of public spaces, and the environmental and acoustic pollution linked to urban logistics. In this context, the optimization of last-mile deliveries is an imperative not only for companies with parcels that need to be delivered in the urban areas, but also for public administrations that want to guarantee a good quality of life for citizens. In recent years, many scholars have focused on the study of logistics optimization techniques and, in particular, the last mile. In addition to traditional optimization techniques, linked to the disciplines of operations research, the recent advances in the use of sensors and IoT, and the consequent large amount of data that derives from it, are pushing towards a greater use of big data and analytics techniques—such as machine learning and artificial intelligence—which are also in this sector. Based on this premise, the aim of this work is to provide an overview of the most recent literature advances related to last-mile delivery optimization techniques; this is to be used as a baseline for scholars who intend to explore new approaches and techniques in the study of last-mile logistics optimization. A bibliometric analysis and a critical review were conducted in order to highlight the main studied problems, the algorithms used, and the case studies. The results from the analysis allow the studies to be clustered into traditional optimization models, machine learning approaches, and mixed methods. The main research gaps and limitations of the current literature are assessed in order to identify unaddressed challenges and provide research suggestions for future approaches.
Optimizing road traffic surveillance: a robust hyper-heuristic approach for vehicle segmentation
(Institute of Electrical and Electronics Engineers Inc., 2024) Rodríguez Esparza, Erick; Ramos Soto, Oscar; Masegosa Arredondo, Antonio David; Onieva Caracuel, Enrique; Oliva, Diego; Arriandiaga Laresgoiti, Ander; Ghosh, Arka
Due to rising consumer demand and traffic congestion, last-mile logistics is becoming more challenging. To optimize urban distribution networks, digital image processing plays a key role in addressing these challenges through efficient traffic monitoring systems, an essential component of intelligent transportation systems. This paper introduces the Hyper-heuristic Genetic Algorithm based on Thompson Sampling with Diversity (HHGATSD), a novel approach to efficiently solving complex optimization and versatility problems in image segmentation. We evaluate its efficiency and robustness using the IEEE CEC2017 benchmark function set in general optimization problems with 30 and 50 dimensions. HHGATSD's applicability extends beyond optimization to computer vision in traffic management. First, the multilevel thresholding segmentation is performed on images extracted from the Berkeley Segmentation Dataset with minimum cross-entropy as the objective function, and its performance is compared using PSNR, SSIM, and FSIM metrics. Following that, the proposed methodology addresses the task of vehicle segmentation in traffic camera videos, reaffirming HHGATSD's effectiveness, adaptability, and consistency by consistently outperforming alternative segmentation methods found in the state-of-the-art. The results of comprehensive experiments, validated by statistical and non-parametric analyses, show that the proposed hyper-heuristic and methodology produce accurate and consistent segmentations for road traffic surveillance compared to the other methods in the literature
Partial evaluation and efficient discarding for the maximal covering location problem
(Institute of Electrical and Electronics Engineers Inc., 2021-01-28) Porras, Cynthia; Fajardo Calderín, Jenny; Rosete, Alejandro; Masegosa Arredondo, Antonio David
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 article, the principles of efficient discarding and partial evaluation are applied to obtain more efficient versions of the objective function of this problem, i.e. not-approximate surrogate objective functions. An experimental study is presented to compare the surrogate functions in terms of number of distance comparisons and runtime. The results show that (on average) the best surrogate function is more than 5 times faster than the original function in general, and more than 8 times faster in the largest instances. This proposal allows for a more efficient metaheuristic solution based on swap operators.
The TANGENT project architecture: towards new traffic management approaches
(Springer, 2026) Landaluce, Hugo ; Serrano, Leire ; Masegosa Arredondo, Antonio David ; Gosh, Arka ; Arrandiaga, Ander; Dias, Tiago; Silva, Ana V. ; Moura, Lara
The TANGENT project (www.tangent-h2020.eu/) aims to address the challenges of urban transportation, including traffic accidents, greenhouse gas emissions, and congestion. The project focuses on optimizing traffic management and enhancing mobility through a distributed, modular, and scalable architecture. TANGENT collects and harmonizes data from various sources, including sensors, users, vehicles, schedules, pricing, and traffic flows. It uses this data to create enriched information for different transport stakeholders. The project combines technologies such as data gathering, travel behavior modeling, traffic prediction and simulation, and transport network optimization to provide advanced transport management services. This paper is focused on presenting the project architecture developed to implement four services: data collection and harmonization, enhanced information service, real-time traffic management, and transport network optimization. The project involves a consortium of organizations from nine European countries and aims to pilot its integrated tool in multiple cities in 2024.
A taxonomy of food supply chain problems from a computational intelligence perspective
(MDPI, 2021-10-18) Angarita Zapata, Juan S. ; Alonso Vicario, Ainhoa; Masegosa Arredondo, Antonio David ; Legarda Macon, Jon
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 intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a datadriven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.
Travel behavior shifts under extreme system-level disruptions
(Elsevier B.V., 2023) Gasparinatou, Christina; Mantouka, Eleni; Vlahogianni, Eleni I.; Masegosa Arredondo, Antonio David ; Serrano, Leire
This paper attempts to identify and critically discuss how travel behaviour may be affected by any extreme system-level conditions of the transportation system in the cities. These disruptions refer to non-recurrent events and indicatively include hazardous events, and perturbations of the road network. To this end, the international literature on the changes in travel behaviour in the case where system-level disruption occur is collected and analysed. The analysis is conducted on the basis of the three pillars of travel behaviour, namely travel mode, route and departure time choices. The results show that most people tend to postpone their trip when extreme weather conditions occur, whereas in case of a public transportation disruption travellers are keen on altering their route choice. Finally, a clear mode shift towards cars has been observed due to the outbreak of COVID-19 pandemic.
Understanding the role of diversity in ensemble-based AutoML methods for classification tasks
(Institute of Electrical and Electronics Engineers Inc., 2025-04-17) Osei, Salomey; Masegosa, Andrés R.; Masegosa Arredondo, Antonio David
Ensemble-based Automated Machine Learning (AutoML) methods have gained prominence for their ability to combine diverse machine learning models, achieving superior generalization performance. Despite their empirical success, the underlying mechanisms driving this performance, particularly the role of model diversity, are not yet adequately understood. This study uses novel theoretical frameworks related to the role of diversity in ensembles, which were recently proposed, to shed light on this issue. In this work, we focus on AutoML methods for classification tasks. We use AUTO-SKLEARN (a widely used AutoML ensemble-based method) as a basis. More specifically, we examine how individual model diversity and performance evolves across the four key phases of AUTO-SKLEARN (base-learners, meta-learning, Bayesian Optimization (BO), and Caruana Ensemble). We also examine how they contribute to the diversity and performance of the final ensemble produced by the AutoML method. Using datasets from the AutoML benchmark, we empirically validate these insights by analyzing error rates and diversity measures across the mentioned phases. Our findings highlight the trade-off between individual model accuracy and ensemble diversity, showing that phases like BO improve the mean error rate of classifiers by nearly 50% percent but reduce their mean diversity by 20%. However, the Caruana phase increases the diversity by a 50% compared to the BO phase, allowing better generalization despite the higher mean error rate of the selected individual models (48% higher than BO). This work provides theoretical and empirical evidence that diversity is critical to the success of ensemble-based AutoML methods and a deeper understanding of diversity’s impact on generalization performance and the role of the different AutoML phases. These findings can contribute to advance the development of more robust and theoretically grounded AutoML frameworks
White-box flight simulator built with system dynamics to support urban transportation decision-making and address induced travel demand
(Universidad Tecnológica de Pereira, 2020-09-30) Angarita Zapata, Juan S.; Andrade Sosa, Hugo Hernando; Masegosa Arredondo, Antonio David
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). However, beyond the contributions of black-box models, it is still necessary to explain structurally this phenomenon for understanding and identifying its causes, which then allow policy-makers to design comprehensive policies to deal with ITD in urban context wherein new roads are still needed to guarantee connectivity. In this paper, we present a white-box flight simulator based on a System Dynamics model to support urban transportation decision-making and address ITD. Through the simulator developed, it is possible to improve the causal understanding of ITD and, although the considered policies to intervene this phenomenon have a conceptual connotation, the simulator is a means to acquire knowledge of the structural complexity underlying the interaction between the policies and ITD.

Examinando por Autor "Masegosa Arredondo, Antonio David"

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