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		<identifier>8JMKD3MGPDW34P/3SEUNGH</identifier>
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		<issn>2179-4847</issn>
		<citationkey>RibeiroFaleHola:2018:GeArDa</citationkey>
		<title>Generating artificial data for bus travel time predictions</title>
		<format>On-line</format>
		<year>2018</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Ribeiro, Leandro S.,</author>
		<author>Faleiros, Thiago P.,</author>
		<author>Holanda, Maristela,</author>
		<affiliation>Universidade de Brasília (UnB)</affiliation>
		<affiliation>Universidade de Brasília (UnB)</affiliation>
		<affiliation>Universidade de Brasília (UnB)</affiliation>
		<editor>Vinhas, Lúbia (INPE),</editor>
		<editor>Campelo, Claudio (UFCG),</editor>
		<e-mailaddress>lubia@dpi.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Geoinformática, 19 (GEOINFO)</conferencename>
		<conferencelocation>Campina Grande</conferencelocation>
		<date>05-07 dez. 2018</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>13-24</pages>
		<transferableflag>1</transferableflag>
		<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 &#769;&#305;lia/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.</abstract>
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		<url>http://mtc-m16c.sid.inpe.br/rep-/sid.inpe.br/mtc-m16c/2018/12.27.17.58</url>
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