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  • 标题:Massive Railway Operating Data Visualization; a Tool for RATP Operating Expert
  • 本地全文:下载
  • 作者:Vincent Dimanche ; Alban Goupil ; Alexandre Philippot
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2017
  • 卷号:50
  • 期号:1
  • 页码:15841-15846
  • DOI:10.1016/j.ifacol.2017.08.2324
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractIn this article we propose a methodology helping RATP (public transport operator) experts to analyze railway big amount of data. Indeed, a huge number of data can rapidly lead to focus on wrong analysis. We developed a tool prototype based on visual analytics process that can display an overview of a selected railway network area. This overview deals with one or several operating days of the same railway line. The operator chooses statistical indicators to obtain a suitable view display for his specific study. This process is the first part of our work which will model the human driver pattern using machine learning and data mining. Our work methodology is based on Visual analytics process.
  • 关键词:KeywordsRailway networkvisual analyticsvisual analytics processbig datadriver modellingmachine learning
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