This page has only limited features, please log in for full access.

Unclaimed
Ibai Lana
Basque Research and Technology Alliance (BRTA), TECNALIA, 48160 Derio, Spain

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 07 June 2021 in IEEE Transactions on Intelligent Transportation Systems
Reads 0
Downloads 0

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems, in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular Intelligent Transportation Systems research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.

ACS Style

Eric L. Manibardo; Ibai Lana; Javier Del Ser. Deep Learning for Road Traffic Forecasting: Does It Make a Difference? IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -25.

AMA Style

Eric L. Manibardo, Ibai Lana, Javier Del Ser. Deep Learning for Road Traffic Forecasting: Does It Make a Difference? IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-25.

Chicago/Turabian Style

Eric L. Manibardo; Ibai Lana; Javier Del Ser. 2021. "Deep Learning for Road Traffic Forecasting: Does It Make a Difference?" IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-25.

Journal article
Published: 05 February 2021 in Sensors
Reads 0
Downloads 0

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

ACS Style

Ibai Laña; Javier Sanchez-Medina; Eleni Vlahogianni; Javier Del Ser. From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability. Sensors 2021, 21, 1121 .

AMA Style

Ibai Laña, Javier Sanchez-Medina, Eleni Vlahogianni, Javier Del Ser. From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability. Sensors. 2021; 21 (4):1121.

Chicago/Turabian Style

Ibai Laña; Javier Sanchez-Medina; Eleni Vlahogianni; Javier Del Ser. 2021. "From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability." Sensors 21, no. 4: 1121.

Journal article
Published: 20 February 2019 in Transportation Research Part C: Emerging Technologies
Reads 0
Downloads 0

Due to the nature of traffic itself, most traffic forecasting models reported in literature aim at producing short-term predictions, yet their performance degrades when the prediction horizon is increased. The scarce long-term estimation strategies currently found in the literature are commonly based on the detection and assignment to patterns, but their performance decays when unexpected events provoke non predictable changes, or if the allocation to a traffic pattern is inaccurate. This work introduces a method to obtain long-term pattern forecasts and adapt them to real-time circumstances. To this end, a long-term estimation scheme based on the automated discovery of patterns is proposed and integrated with an on-line change detection and adaptation mechanism. The framework takes advantage of the architecture of evolving Spiking Neural Networks (eSNN) to perform adaptations without retraining the model, allowing the whole system to work autonomously in an on-line fashion. Its performance is assessed over a real scenario with 5 min data of a 6-month span of traffic in the center of Madrid, Spain. Significant accuracy gains are obtained when applying the proposed on-line adaptation mechanism on days with special, non-predictable events that degrade the quality of their long-term traffic forecasts.

ACS Style

Ibai Laña; Jesús López Lobo; Elisa Capecci; Javier Del Ser; Nikola Kasabov. Adaptive long-term traffic state estimation with evolving spiking neural networks. Transportation Research Part C: Emerging Technologies 2019, 101, 126 -144.

AMA Style

Ibai Laña, Jesús López Lobo, Elisa Capecci, Javier Del Ser, Nikola Kasabov. Adaptive long-term traffic state estimation with evolving spiking neural networks. Transportation Research Part C: Emerging Technologies. 2019; 101 ():126-144.

Chicago/Turabian Style

Ibai Laña; Jesús López Lobo; Elisa Capecci; Javier Del Ser; Nikola Kasabov. 2019. "Adaptive long-term traffic state estimation with evolving spiking neural networks." Transportation Research Part C: Emerging Technologies 101, no. : 126-144.

Conference paper
Published: 17 November 2018 in Lecture Notes in Computer Science
Reads 0
Downloads 0
ACS Style

Durgesh Nandini; Elisa Capecci; Lucien Koefoed; Ibai Laña; Gautam Kishore Shahi; Nikola Kasabov. Modelling and Analysis of Temporal Gene Expression Data Using Spiking Neural Networks. Lecture Notes in Computer Science 2018, 571 -581.

AMA Style

Durgesh Nandini, Elisa Capecci, Lucien Koefoed, Ibai Laña, Gautam Kishore Shahi, Nikola Kasabov. Modelling and Analysis of Temporal Gene Expression Data Using Spiking Neural Networks. Lecture Notes in Computer Science. 2018; ():571-581.

Chicago/Turabian Style

Durgesh Nandini; Elisa Capecci; Lucien Koefoed; Ibai Laña; Gautam Kishore Shahi; Nikola Kasabov. 2018. "Modelling and Analysis of Temporal Gene Expression Data Using Spiking Neural Networks." Lecture Notes in Computer Science , no. : 571-581.

Conference paper
Published: 01 November 2018 in 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Reads 0
Downloads 0

This paper focuses on modeling and solving a last-mile package delivery routing problem with third-party drop-off points. The study is applicable to small or medium-sized delivery companies, which use bikes for performing the routes in an influence area bounded to a city. This routing setup has been formulated as a multi-objective optimization problem, balancing three conflicting objectives: a weighted measure of distance of the route, the safety of the biker, and the economic profit yielded by the delivery of goods to customers. Six different and heterogeneous multi-objective algorithms have been applied to the modeled problem: NSGA-II, MOCell, SMPSO, MOEA/D, NSGA-III and MOMBI2. In order to evaluate the performance of these algorithms, we have devised three experimental setups encompassing different real localizations in Madrid (Spain). For deploying a realistic simulation platform, the open-source Open Trip Planner framework has been used as a proxy evaluator of the produced routes. Results have been compared using the obtained Median and Inter Quartile Range of the hypervolume values reached by the algorithms. Conclusions drawn from this study show that MOCell is the best method for the proposed problem, reaching routes that balance the considered three objectives in a more Pareto-optimal fashion than the rest of counterparts in the benchmark.

ACS Style

Eneko Osaba; Javier Del Ser; Antonio J. Nebro; Ibai Lana; Miren Nekane Bilbao; Javier J. Sanchez-Medina. Multi-Objective Optimization of Bike Routes for Last-Mile Package Delivery with Drop-Offs. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018, 865 -870.

AMA Style

Eneko Osaba, Javier Del Ser, Antonio J. Nebro, Ibai Lana, Miren Nekane Bilbao, Javier J. Sanchez-Medina. Multi-Objective Optimization of Bike Routes for Last-Mile Package Delivery with Drop-Offs. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). 2018; ():865-870.

Chicago/Turabian Style

Eneko Osaba; Javier Del Ser; Antonio J. Nebro; Ibai Lana; Miren Nekane Bilbao; Javier J. Sanchez-Medina. 2018. "Multi-Objective Optimization of Bike Routes for Last-Mile Package Delivery with Drop-Offs." 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , no. : 865-870.

Conference paper
Published: 15 September 2018 in Intelligent Distributed Computing XII
Reads 0
Downloads 0

Drift detection in changing environments is a key factor for those active adaptive methods which require trigger mechanisms for drift adaptation. Most approaches are relied on a base learner that provides accuracies or error rates to be analyzed by an algorithm. In this work we propose the use of evolving spiking neural networks as a new form of drift detection, which resorts to the own architectural changes of this particular class of models to estimate the drift location without requiring any external base learner. By virtue of its inherent simplicity and lower computational cost, this embedded approach can be suitable for its adoption in online learning scenarios with severe resource constraints. Experiments with synthetic datasets show that the proposed technique is very competitive when compared to other drift detection techniques.

ACS Style

Jesus L. Lobo; Javier Del Ser; Ibai Laña; Miren Nekane Bilbao; Nikola Kasabov. Drift Detection over Non-stationary Data Streams Using Evolving Spiking Neural Networks. Intelligent Distributed Computing XII 2018, 82 -94.

AMA Style

Jesus L. Lobo, Javier Del Ser, Ibai Laña, Miren Nekane Bilbao, Nikola Kasabov. Drift Detection over Non-stationary Data Streams Using Evolving Spiking Neural Networks. Intelligent Distributed Computing XII. 2018; ():82-94.

Chicago/Turabian Style

Jesus L. Lobo; Javier Del Ser; Ibai Laña; Miren Nekane Bilbao; Nikola Kasabov. 2018. "Drift Detection over Non-stationary Data Streams Using Evolving Spiking Neural Networks." Intelligent Distributed Computing XII , no. : 82-94.

Conference paper
Published: 15 September 2018 in Econometrics for Financial Applications
Reads 0
Downloads 0

This paper presents a new approach for spatio-temporal road traffic forecasting that relies on the adoption of the NeuCube architecture based on spiking neural networks. The NeuCube platform was originally conceived and designed to process electroencephalographic (EEG) signals considering their temporal component and their spatial source within the brain. Its neural representation allows for a visual analysis of connectivity among different locations, and also provides a prediction tool harnessing the predictive learning capabilities of dynamic evolving Spiking Neural Networks (deSNNs). Taking advantage of the NeuCube features, this work focuses on the potential of spatially-aware traffic variable forecasts, as well as on the exploration of the spatio-temporal relationships among different sensor locations within a traffic network. Its performance, assessed over real traffic data collected in 51 locations in the center of Madrid (Spain), is superior to that of other machine learning techniques in terms of forecasting accuracy. Moreover, we discuss on the interactions and relationships among sensors of the network provided by Neucube, which may provide valuable insights on the traffic dynamics of the city under study towards enhancing its management.

ACS Style

Ibai Laña; Elisa Capecci; Javier Del Ser; Jesús López Lobo; Nikola Kasabov. Road Traffic Forecasting Using NeuCube and Dynamic Evolving Spiking Neural Networks. Econometrics for Financial Applications 2018, 192 -203.

AMA Style

Ibai Laña, Elisa Capecci, Javier Del Ser, Jesús López Lobo, Nikola Kasabov. Road Traffic Forecasting Using NeuCube and Dynamic Evolving Spiking Neural Networks. Econometrics for Financial Applications. 2018; ():192-203.

Chicago/Turabian Style

Ibai Laña; Elisa Capecci; Javier Del Ser; Jesús López Lobo; Nikola Kasabov. 2018. "Road Traffic Forecasting Using NeuCube and Dynamic Evolving Spiking Neural Networks." Econometrics for Financial Applications , no. : 192-203.

Review article
Published: 02 August 2018 in Neural Networks
Reads 0
Downloads 0

Nowadays huges volumes of data are produced in the form of fast streams, which are further affected by non-stationary phenomena. The resulting lack of stationarity in the distribution of the produced data calls for efficient and scalable algorithms for online analysis capable of adapting such changes (concept drift). The online learning field has lately turned its focus on this challenging scenario, by designing incremental learning algorithms that avoid becoming obsolete after a concept drift occurs. Despite the noted activity in the literature, a need for new efficient and scalable algorithms that adapt to the drift still prevails as a research topic deserving further effort. Surprisingly, Spiking Neural Networks, one of the major exponents of the third generation of artificial neural networks, have not been thoroughly studied as an online learning approach, even though they are naturally suited to easily and quickly adapting to changing environments. This work covers this research gap by adapting Spiking Neural Networks to meet the processing requirements that online learning scenarios impose. In particular the work focuses on limiting the size of the neuron repository and making the most of this limited size by resorting to data reduction techniques. Experiments with synthetic and real data sets are discussed, leading to the empirically validated assertion that, by virtue of a tailored exploitation of the neuron repository, Spiking Neural Networks adapt better to drifts, obtaining higher accuracy scores than naive versions of Spiking Neural Networks for online learning environments.

ACS Style

Jesus L. Lobo; Ibai Laña; Javier Del Ser; Miren Nekane Bilbao; Nikola Kasabov. Evolving Spiking Neural Networks for online learning over drifting data streams. Neural Networks 2018, 108, 1 -19.

AMA Style

Jesus L. Lobo, Ibai Laña, Javier Del Ser, Miren Nekane Bilbao, Nikola Kasabov. Evolving Spiking Neural Networks for online learning over drifting data streams. Neural Networks. 2018; 108 ():1-19.

Chicago/Turabian Style

Jesus L. Lobo; Ibai Laña; Javier Del Ser; Miren Nekane Bilbao; Nikola Kasabov. 2018. "Evolving Spiking Neural Networks for online learning over drifting data streams." Neural Networks 108, no. : 1-19.

Review article
Published: 17 July 2018 in IET Intelligent Transport Systems
Reads 0
Downloads 0

Big Data is an emerging paradigm and has currently become a strong attractor of global interest, specially within the transportation industry. The combination of disruptive technologies and new concepts such as the Smart City upgrades the transport data life cycle. In this context, Big Data is considered as a new pledge for the transportation industry to effectively manage all data this sector required for providing safer, cleaner and more efficient transport means, as well as for users to personalize their transport experience. However, Big Data comes along with its own set of technological challenges, stemming from the multiple and heterogeneous transportation/mobility application scenarios. In this survey we analyze the latest research efforts revolving on Big Data for the transportation and mobility industry, its applications, baselines scenarios, fields and use case such as routing, planning, infrastructure monitoring, network design, among others. This analysis will be done strictly from the Big Data perspective, focusing on those contributions gravitating on techniques, tools and methods for modeling, processing, analyzing and visualizing transport and mobility Big Data. From the literature review a set of trends and challenges is extracted so as to provide researchers with an insightful outlook on the field of transport and mobility.

ACS Style

Ana Isabel Torre‐Bastida; Javier Del Ser; Ibai Laña; Maitena Ilardia; Miren Nekane Bilbao; Sergio Campos‐Cordobés. Big Data for transportation and mobility: recent advances, trends and challenges. IET Intelligent Transport Systems 2018, 12, 742 -755.

AMA Style

Ana Isabel Torre‐Bastida, Javier Del Ser, Ibai Laña, Maitena Ilardia, Miren Nekane Bilbao, Sergio Campos‐Cordobés. Big Data for transportation and mobility: recent advances, trends and challenges. IET Intelligent Transport Systems. 2018; 12 (8):742-755.

Chicago/Turabian Style

Ana Isabel Torre‐Bastida; Javier Del Ser; Ibai Laña; Maitena Ilardia; Miren Nekane Bilbao; Sergio Campos‐Cordobés. 2018. "Big Data for transportation and mobility: recent advances, trends and challenges." IET Intelligent Transport Systems 12, no. 8: 742-755.

Journal article
Published: 23 April 2018 in IEEE Intelligent Transportation Systems Magazine
Reads 0
Downloads 0

Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. More recently, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. This paper aims to summarize the efforts made to date in previous related surveys towards extracting the main comparing criteria and challenges in this field. A review of the latest technical achievements in this field is also provided, along with an insightful update of the main technical challenges that remain unsolved. The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.

ACS Style

Ibai Lana; Javier Del Ser; Manuel Velez; Eleni I. Vlahogianni. Road Traffic Forecasting: Recent Advances and New Challenges. IEEE Intelligent Transportation Systems Magazine 2018, 10, 93 -109.

AMA Style

Ibai Lana, Javier Del Ser, Manuel Velez, Eleni I. Vlahogianni. Road Traffic Forecasting: Recent Advances and New Challenges. IEEE Intelligent Transportation Systems Magazine. 2018; 10 (2):93-109.

Chicago/Turabian Style

Ibai Lana; Javier Del Ser; Manuel Velez; Eleni I. Vlahogianni. 2018. "Road Traffic Forecasting: Recent Advances and New Challenges." IEEE Intelligent Transportation Systems Magazine 10, no. 2: 93-109.

Book chapter
Published: 01 January 2018 in Intelligent Vehicles
Reads 0
Downloads 0
ACS Style

Sergio Campos-Cordobés; Javier Del Ser; Ibai Laña; Ignacio (Iñaki) Olabarrieta; Javier Sánchez-Cubillo; Javier J. Sánchez-Medina; Ana I. Torre-Bastida. Big Data in Road Transport and Mobility Research. Intelligent Vehicles 2018, 175 -205.

AMA Style

Sergio Campos-Cordobés, Javier Del Ser, Ibai Laña, Ignacio (Iñaki) Olabarrieta, Javier Sánchez-Cubillo, Javier J. Sánchez-Medina, Ana I. Torre-Bastida. Big Data in Road Transport and Mobility Research. Intelligent Vehicles. 2018; ():175-205.

Chicago/Turabian Style

Sergio Campos-Cordobés; Javier Del Ser; Ibai Laña; Ignacio (Iñaki) Olabarrieta; Javier Sánchez-Cubillo; Javier J. Sánchez-Medina; Ana I. Torre-Bastida. 2018. "Big Data in Road Transport and Mobility Research." Intelligent Vehicles , no. : 175-205.

Conference paper
Published: 01 June 2017 in 2017 IEEE Congress on Evolutionary Computation (CEC)
Reads 0
Downloads 0

This work focuses on wide-scale freight transportation logistics motivated by the sharp increase of on-line shopping stores and the upsurge of Internet as the most frequently utilized selling channel during the last decade. This huge ecosystem of one-click-away catalogs has ultimately unleashed the need for efficient algorithms aimed at properly scheduling the underlying transportation resources in an efficient fashion, especially over the so-called last mile of the distribution chain. In this context the selective pickup and delivery problem focuses on determining the optimal subset of packets that should be picked from its origin city and delivered to their corresponding destination within a given time frame, often driven by the maximization of the total profit of the courier service company. This manuscript tackles a realistic variant of this problem where the transportation fleet is composed by more than one vehicle, which further complicates the selection of packets due to the subsequent need for coordinating the delivery service from the command center. In particular the addressed problem includes a second optimization metric aimed at reflecting a fair share of the net benefit among the company staff based on their driven distance. To efficiently solve this optimization problem, several nature-inspired metaheuristic solvers are analyzed and statistically compared to each other under different parameters of the problem setup. Finally, results obtained over a realistic scenario over the province of Bizkaia (Spain) using emulated data will be explored so as to shed light on the practical applicability of the analyzed heuristics.

ACS Style

Javier Del Ser; Ana I. Torre-Bastida; Ibai Laña; Miren Nekane Bilbao; Cristina Perfecto. Nature-inspired heuristics for the multiple-vehicle selective pickup and delivery problem under maximum profit and incentive fairness criteria. 2017 IEEE Congress on Evolutionary Computation (CEC) 2017, 480 -487.

AMA Style

Javier Del Ser, Ana I. Torre-Bastida, Ibai Laña, Miren Nekane Bilbao, Cristina Perfecto. Nature-inspired heuristics for the multiple-vehicle selective pickup and delivery problem under maximum profit and incentive fairness criteria. 2017 IEEE Congress on Evolutionary Computation (CEC). 2017; ():480-487.

Chicago/Turabian Style

Javier Del Ser; Ana I. Torre-Bastida; Ibai Laña; Miren Nekane Bilbao; Cristina Perfecto. 2017. "Nature-inspired heuristics for the multiple-vehicle selective pickup and delivery problem under maximum profit and incentive fairness criteria." 2017 IEEE Congress on Evolutionary Computation (CEC) , no. : 480-487.

Proceedings article
Published: 01 June 2017 in 2017 IEEE Congress on Evolutionary Computation (CEC)
Reads 0
Downloads 0

Fireworks Algorithm (FWA) is a recently contributed heuristic optimization method that has shown a promising performance in applications stemming from different domains. Improvements to the original algorithm have been designed and tested in the related literature. Nonetheless, in most of such previous works FWA has been tested with standard test functions, hence its performance when applied to real application cases has been scarcely assessed. In this manuscript a mechanism for accelerating the convergence of this meta-heuristic is proposed based on observed wind inertia dynamics (WID) among fireworks in practice. The resulting enhanced algorithm will be described algorithmically and evaluated in terms of convergence speed by means of test functions. As an additional novel contribution of this work FWA and FWA-WID are used in a practical application where such heuristics are used as wrappers for optimizing the parameters of a road traffic short-term predictive model. The exhaustive performance analysis of the FWA and FWA-ID in this practical setup has revealed that the relatively high computational complexity of this solver with respect to other heuristics makes it critical to speed up their convergence (specially in cases with a costly fitness evaluation as the one tackled in this work), observation that buttresses the utility of the proposed modifications to the naive FWA solver.

ACS Style

Ibai Lana; Javier Del Ser; Manuel Velez. A novel Fireworks Algorithm with wind inertia dynamics and its application to traffic forecasting. 2017 IEEE Congress on Evolutionary Computation (CEC) 2017, 706 -713.

AMA Style

Ibai Lana, Javier Del Ser, Manuel Velez. A novel Fireworks Algorithm with wind inertia dynamics and its application to traffic forecasting. 2017 IEEE Congress on Evolutionary Computation (CEC). 2017; ():706-713.

Chicago/Turabian Style

Ibai Lana; Javier Del Ser; Manuel Velez. 2017. "A novel Fireworks Algorithm with wind inertia dynamics and its application to traffic forecasting." 2017 IEEE Congress on Evolutionary Computation (CEC) , no. : 706-713.

Conference paper
Published: 29 January 2017 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

Short-term traffic flow forecasting is a vibrant research topic that has been growing in interest since the late 70’s. In the last decade this vibrant field has shifted its focus towards machine learning methods. These techniques often require fine-grained parameter tuning to obtain satisfactory performance scores, a process that usually relies on manual trial-and-error adjustment. This paper explores the use of Harmony Search optimization for tuning the parameters of neural network jointly with the selection of the input features from the dataset at hand. Results are discussed and compared to other tuning methods, from which it is concluded that neural predictors optimized via the proposed heuristic wrapper outperform those tuned by means of naïve parametrized algorithms, thus allowing for longer-term predictions. These promising results unfold potential applications of this technique in multi-location neighbor-aware traffic prediction.

ACS Style

Ibai Laña; Javier Del Ser; Manuel Velez; Izaskun Oregi. Joint Feature Selection and Parameter Tuning for Short-Term Traffic Flow Forecasting Based on Heuristically Optimized Multi-layer Neural Networks. Advances in Intelligent Systems and Computing 2017, 91 -100.

AMA Style

Ibai Laña, Javier Del Ser, Manuel Velez, Izaskun Oregi. Joint Feature Selection and Parameter Tuning for Short-Term Traffic Flow Forecasting Based on Heuristically Optimized Multi-layer Neural Networks. Advances in Intelligent Systems and Computing. 2017; ():91-100.

Chicago/Turabian Style

Ibai Laña; Javier Del Ser; Manuel Velez; Izaskun Oregi. 2017. "Joint Feature Selection and Parameter Tuning for Short-Term Traffic Flow Forecasting Based on Heuristically Optimized Multi-layer Neural Networks." Advances in Intelligent Systems and Computing , no. : 91-100.

Conference paper
Published: 29 January 2017 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

This paper describes a Big Data stream analytics platform developed within the DEWI project for processing upcoming events from wireless sensors installed in a truck. The platform consists of a Complex Event Processing (CEP) engine capable of triggering alarms from a predefined set of rules. In general these rules are characterized by multiple parameters, for which finding their optimal value usually yields a challenging task. In this paper we explain a methodology based on a meta-heuristic solver that is used as a wrapper to obtain optimal parametric rules for the CEP engine. In particular this approach optimizes CEP rules through the refinement of the parameters controlling their behavior based on an alarm detection improvement criterion. As a result the proposed scheme retrieves the rules parameterized in a detection-optimal fashion. Results for a certain use case – i.e. fuel level of the vehicle – are discussed towards assessing the performance gains provided by our method.

ACS Style

Ignacio (Iñaki) Olabarrieta; Ana I. Torre-Bastida; Ibai Laña; Sergio Campos-Cordobes; Javier Del Ser. A Heuristically Optimized Complex Event Processing Engine for Big Data Stream Analytics. Advances in Intelligent Systems and Computing 2017, 101 -111.

AMA Style

Ignacio (Iñaki) Olabarrieta, Ana I. Torre-Bastida, Ibai Laña, Sergio Campos-Cordobes, Javier Del Ser. A Heuristically Optimized Complex Event Processing Engine for Big Data Stream Analytics. Advances in Intelligent Systems and Computing. 2017; ():101-111.

Chicago/Turabian Style

Ignacio (Iñaki) Olabarrieta; Ana I. Torre-Bastida; Ibai Laña; Sergio Campos-Cordobes; Javier Del Ser. 2017. "A Heuristically Optimized Complex Event Processing Engine for Big Data Stream Analytics." Advances in Intelligent Systems and Computing , no. : 101-111.

Journal article
Published: 01 November 2016 in Atmospheric Environment
Reads 0
Downloads 0

Urban air pollution is a matter of growing concern for both public administrations and citizens. Road traffic is one of the main sources of air pollutants, though topography characteristics and meteorological conditions can make pollution levels increase or diminish dramatically. In this context an upsurge of research has been conducted towards functionally linking variables of such domains to measured pollution data, with studies dealing with up to one-hour resolution meteorological data. However, the majority of such reported contributions do not deal with traffic data or, at most, simulate traffic conditions jointly with the consideration of different topographical features. The aim of this study is to further explore this relationship by using high-resolution real traffic data. This paper describes a methodology based on the construction of regression models to predict levels of different pollutants (i.e. CO, NO, NO2, O3 and PM10) based on traffic data and meteorological conditions, from which an estimation of the predictive relevance (importance) of each utilized feature can be estimated by virtue of their particular training procedure. The study was made with one hour resolution meteorological, traffic and pollution historic data in roadside and background locations of the city of Madrid (Spain) captured over 2015. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowed by the effects of stable meteorological conditions of this city.This work has been funded in part by the Basque Government under the ELKARTEK program (BID3A project, grant ref. KK-2015/0000080)

ACS Style

Ibai Laña; Javier Del Ser; Ales Padró; Manuel Velez; Carlos Casanova-Mateo. The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain. Atmospheric Environment 2016, 145, 424 -438.

AMA Style

Ibai Laña, Javier Del Ser, Ales Padró, Manuel Velez, Carlos Casanova-Mateo. The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain. Atmospheric Environment. 2016; 145 ():424-438.

Chicago/Turabian Style

Ibai Laña; Javier Del Ser; Ales Padró; Manuel Velez; Carlos Casanova-Mateo. 2016. "The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain." Atmospheric Environment 145, no. : 424-438.

Conference paper
Published: 08 October 2016 in Econometrics for Financial Applications
Reads 0
Downloads 0

In the last decade the interest in adaptive models for non-stationary environments has gained momentum within the research community due to an increasing number of application scenarios generating non-stationary data streams. In this context the literature has been specially rich in terms of ensemble techniques, which in their majority have focused on taking advantage of past information in the form of already trained predictive models and other alternatives alike. This manuscript elaborates on a rather different approach, which hinges on extracting the essential predictive information of past trained models and determining therefrom the best candidates (intelligent sample matchmaking) for training the predictive model of the current data batch. This novel perspective is of inherent utility for data streams characterized by short-length unbalanced data batches, situation where the so-called trade-off between plasticity and stability must be carefully met. The approach is evaluated on a synthetic data set that simulates a non-stationary environment with recurrently changing concept drift. The proposed approach is shown to perform competitively when adapting to a sudden and recurrent change with respect to the state of the art, but without storing all the past trained models and by lessening its computational complexity in terms of model evaluations. These promising results motivate future research aimed at validating the proposed strategy on other scenarios under concept drift, such as those characterized by semi-supervised data streams.

ACS Style

Jesus L. Lobo; Javier Del Ser; Miren Nekane Bilbao; Ibai Laña; S. Salcedo-Sanz. A Probabilistic Sample Matchmaking Strategy for Imbalanced Data Streams with Concept Drift. Econometrics for Financial Applications 2016, 678, 237 -246.

AMA Style

Jesus L. Lobo, Javier Del Ser, Miren Nekane Bilbao, Ibai Laña, S. Salcedo-Sanz. A Probabilistic Sample Matchmaking Strategy for Imbalanced Data Streams with Concept Drift. Econometrics for Financial Applications. 2016; 678 ():237-246.

Chicago/Turabian Style

Jesus L. Lobo; Javier Del Ser; Miren Nekane Bilbao; Ibai Laña; S. Salcedo-Sanz. 2016. "A Probabilistic Sample Matchmaking Strategy for Imbalanced Data Streams with Concept Drift." Econometrics for Financial Applications 678, no. : 237-246.

Conference paper
Published: 01 April 2016 in NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
Reads 0
Downloads 0

The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range of traffic flow prediction methods has been reported in the related literature. However, most of the efforts in this field have been hitherto focused on short-term prediction models. This paper analyzes how to properly characterize traffic flow in urban road scenarios with an emphasis on the long term. To this end a clustering stage is utilized to discover typicalities or patterns within the traffic flow data registered by each road sensor, which permits building prediction models for each of such discovered patterns. These individual prediction models are intended to become part of the MoveUs platform, which will provide the technical means 1) for traffic managers to analyze in depth the status of the road network, and 2) for road users to better plan their trips.

ACS Style

Ibai Laña; Javier Del Ser; Ignacio Iñaki Olabarrieta. Understanding daily mobility patterns in urban road networks using traffic flow analytics. NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium 2016, 1157 -1162.

AMA Style

Ibai Laña, Javier Del Ser, Ignacio Iñaki Olabarrieta. Understanding daily mobility patterns in urban road networks using traffic flow analytics. NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. 2016; ():1157-1162.

Chicago/Turabian Style

Ibai Laña; Javier Del Ser; Ignacio Iñaki Olabarrieta. 2016. "Understanding daily mobility patterns in urban road networks using traffic flow analytics." NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium , no. : 1157-1162.