Browsing by Author "del Ser, Javier"
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Item Big data in road transport and mobility research(Elsevier, 2017-01-01) Campos-Cordobés, Sergio; del Ser, Javier; Laña, Ibai; Olabarrieta, Ignacio Iñaki; Sánchez-Cubillo, Javier; Sánchez-Medina, Javier J.; Torre-Bastida, Ana I.; LABORATORIO DE TRANSFORMACIÓN URBANA; SMART_TRANSPORT; IA; Tecnalia Research & Innovation; HPAUbiquitous computing has changed the acquisition of mobility data, with two aspects contributing: the high penetration rate and the ability to capture and share information on a continuous basis. This applies to geolocation information, operational mobile phone data, and also, social network crowdsourced information. Additionally, under the umbrella of the Internet of Things trend, the deployment of the Connected Vehicle (Car-as-a-sensor) concept, supported by advanced V2X communications, provides massive data volume. For all these cases, data are open to never before seen opportunities to analyze and predict individual and aggregated mobility patterns. Big Data refers to the processsing capabilities of such an explosion in the amount, quality, and heterogeneity of available data. This chapter will review the most relevant data sources, introduce the underlying techniques supporting the BigData paradigm and, finally, provide a list of some relevant applications in the transport and mobility domain.Item Environmental perception for intelligent vehicles(Elsevier, 2017-01-01) Armingol, José M.; Alfonso, Jorge; Aliane, Nourdine; Clavijo, Miguel; Campos-Cordobés, Sergio; de la Escalera, Arturo; del Ser, Javier; Fernández, Javier; García, Fernando; Jiménez, Felipe; López, Antonio M.; Mata, Mario; Martín, David; Menéndez, José M.; Sánchez-Cubillo, Javier; Vázquez, David; Villalonga, Gabriel; LABORATORIO DE TRANSFORMACIÓN URBANA; SMART_TRANSPORT; Tecnalia Research & InnovationEnvironmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems.Item A novel harmony search algorithm for one-year-ahead energy demand estimation using macroeconomic variables(Springer Verlag, 2014) Salcedo-Sanz, Sancho; Portilla-Figueras, Joé Antonio; Muñoz-Bulnes, Jesús; del Ser, Javier; Bilbao, Miren Nekane; Klett, Fanny; Abraham, Ajith; Herrero, Álvaro; Baruque, Bruno; de Carvalho, André C.P.L.F.; Quintián, Héctor; Corchado, Emilio; Quintián, Héctor; de la Puerta, José Gaviria; Ferreira, Iván García; Bringas, Pablo García; IAIn this paper we tackle a problem of one-year ahead energy demand estimation from macroeconomic variables. A modified Harmony Search (HS) algorithm is proposed to this end, as one of the novelties of the paper. The modifications on the proposed HS include a hybrid encoding, with a binary part to carry out a feature selection, and a real part, to select the parameter of a given prediction model. Some other adaptation focussed on the HS operators are also introduced. We study the performance of the proposed approach in a real problem of Energy demand estimation in Spain, from 14 macroeconomic variables with values for the last 30 years, including years of the crisis, from 2008. The performance of the proposed HS with feature selection is excellent, providing an accurate one year ahead prediction that improves previous proposals in the literature.Item Positioning and digital maps(Elsevier, 2017-01-01) Toledo-Moreo, Rafael; Armingol, José M.; Clavijo, Miguel; de la Escalera, Arturo; del Ser, Javier; Jiménez, Felipe; Musleh, Basam; Naranjo, José E.; Olabarrieta, Ignacio Iñaki; Sánchez-Cubillo, Javier; IA; Tecnalia Research & InnovationA reliable positioning system is essential for the development of intelligent vehicles. This chapter provides an overview of different technologies and techniques that are crucial to understand modern positioning systems onboard road vehicles. It is written for the purpose of serving as a guide to students, engineers, and researchers in the field of vehicular technology or Intelligent Transportation Systems. In Section 4.1, after an introduction to the problem, a handy list of key definitions is provided, and some of the most relevant Location-Based Services mentioned. Section 4.2 presents the fundamentals of GNSS-based positioning. Aiding technologies, such as odometers and inertial sensors, and techniques for GNSS-based hybridized positioning are discussed in Section 4.3. Later, Section 4.4 analyzes the role of digital maps, map-matching, and map-aided positioning. Finally, Section 4.5 introduces alternatives to GNSS, such as visual odometry, with a brief mention of wireless networks and RFID.