@InProceedings{RibeiroFaleHola:2018:GeArDa,
               author = "Ribeiro, Leandro S. and Faleiros, Thiago P. and Holanda, 
                         Maristela",
          affiliation = "{Universidade de Bras{\'{\i}}lia (UnB)} and {Universidade de 
                         Bras{\'{\i}}lia (UnB)} and {Universidade de Bras{\'{\i}}lia 
                         (UnB)}",
                title = "Generating artificial data for bus travel time predictions",
                 year = "2018",
               editor = "Vinhas, L{\'u}bia (INPE) and Campelo, Claudio (UFCG)",
                pages = "13--24",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 19. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This paper proposes a simulator capable of quickly generating a 
                         large amount of data that may be used to train bus travel time 
                         predictive algorithms in an urban transport network. To validate 
                         the proposal, a case study was car- ried out on a bus line in the 
                         city of Bras \́\ılia/DF, Brazil. In the case study, 
                         the Simulator generated data for several scenarios that differ in 
                         distinct levels of variability and these data were used to 
                         evaluate the performance of a K-Nearest Neighbor predictor in each 
                         of the scenarios.",
  conference-location = "Campina Grande",
      conference-year = "05-07 dez. 2018",
                 issn = "2179-4847",
             language = "pt",
                  ibi = "8JMKD3MGPDW34P/3SEUNGH",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3SEUNGH",
           targetfile = "p2.pdf",
        urlaccessdate = "28 abr. 2024"
}