• A comparison of deep learning methods for urban traffic forecasting using floating car data 

      Vázquez Giménez, Juan José; Arjona Martínez, Jamie; Linares Herreros, María Paz; Casanovas Garcia, Josep (Elsevier, 2020)
      Article
      Accés obert
      Cities today must address the challenge of sustainable mobility, and traffic state forecasting plays a key role in mitigating traffic congestion in urban areas. For example, predicting path travel time is a crucial issue ...
    • Characterizing parking systems from sensor data through a data-driven approach 

      Arjona Martínez, Jamie; Linares Herreros, María Paz; Casanovas Garcia, Josep (Informa UK (Taylor & Francis), 2021)
      Article
      Accés restringit per política de l'editorial
      Nowadays, urban traffic affects the quality of life in cities as the problem becomes even more exacerbated by parking issues: congestion increases due to drivers searching slots to park. An Internet of Things approach ...
    • Designing smart ITS services through innovative data analysis modeling 

      Arjona Martínez, Jamie (Universitat Politècnica de Catalunya, 2021-02-11)
      Tesi
      Accés obert
      Nowadays, one of the most important problems in urban areas concerns traffic congestion. This, in turn, has an impact on the economy, nature, human health, city architecture, and many other facets of life. Part of the ...
    • Improving parking availability information using deep learning techniques 

      Arjona Martínez, Jamie; Linares Herreros, María Paz; Casanovas Garcia, Josep; Vázquez Giménez, Juan José (Elsevier, 2020)
      Article
      Accés obert
      Urban traffic currently affects the quality of life in cities and metropolitan areas as the problem becomes ever more aggravated by parking issues: congestion increases due to individuals looking for slotsto park their ...
    • Indústria 4.0: integrant un simulador en un sistema de control d'AGV 

      Arjona Martínez, Jamie (Universitat Politècnica de Catalunya, 2016-09-28)
      Projecte Final de Màster Oficial
      Accés restringit per acord de confidencialitat