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@InProceedings{GomesSantVida:2017:InViTr,
               author = "Gomes, George Allan Menezes and Santos, Emanuele and Vidal, Creto 
                         A.",
          affiliation = "Federal University of Cear{\'a}, Fortaleza, Brazil and Federal 
                         University of Cear{\'a}, Fortaleza, Brazil and Federal University 
                         of Cear{\'a}, Fortaleza, Brazil",
                title = "Interactive Visualization of Traffic Dynamics Based on Trajectory 
                         Data",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "interactive visualization, traffic visualization, traffic 
                         patterns, spatiotemporal, visual exploration.",
             abstract = "Urbanization is accelerating worldwide, giving rise to serious 
                         traffic problems. Traffic wave, known as stop-and-go traffic or 
                         phantom intersection, is one of the most significant traffic 
                         oscillation patterns studied in Traffic Engineering. Usually these 
                         studies are numerical experiments that investigate specific 
                         places, such as a crossroad or a highway section, and their 
                         findings cannot, therefore, be easily applied to sensing device 
                         data in a systematic computational approach. In this regard, 
                         visual analytics can help by combining automated analysis with 
                         interactive visualization for effective understanding, reasoning, 
                         and decision-making. In this paper, we present a novel approach 
                         for visualizing traffic oscillation patterns by visualizing the 
                         objects' movement in space over time, inspired by vector field 
                         visualization. We propose an algorithm to control and synchronize 
                         the visualization time; a systematic stepwise methodology for 
                         exploring sensing device data; and a visualization tool that 
                         computes the trajectory data in parallel on the GPU at interactive 
                         frame rates. Moreover, our approach is designed to support both 
                         batch-processed and streaming data applications. We also present 
                         the benefits and limitations of our visualization proposal based 
                         on domain expert feedback. Finally, we present performance tests 
                         with very encouraging results to support our approach.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4959881.pdf",
        urlaccessdate = "2021, Jan. 25"
}


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