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- W2964439029 abstract "France has the second largest European railway network, with a total of 29,901 kilometers of railway. However, the travel experience of passengers is frequently marked by delays, late arrival of trains at stations, causing inconvenience. The purpose of this paper is to present a new approach for visual prediction of train delays. Our approach is driven by predictive analysis and interactive visualization. The study has benefitted from access to open data SNCF including information about train delays , train number , station , departure and arrival time .Based on this data we develop a new workflow for predictive analysis including visualization in all steps from data pre-processing to deployment ." @default.
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- W2964439029 date "2018-11-24" @default.
- W2964439029 modified "2023-09-27" @default.
- W2964439029 title "Using visualization and predictive analysis to predict train delays" @default.
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- W2964439029 doi "https://doi.org/10.21533/pen.v6i2.537" @default.
- W2964439029 hasPublicationYear "2018" @default.
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