Using visualization and predictive analysis to predict train delays

Sara lbazri, Younes Oubrahim, Amina Rachik, Mohamed Azouazi

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 .

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DOI: http://dx.doi.org/10.21533/pen.v6i2.537

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Copyright (c) 2019 sara lbazri, younes oubrahim, amina rachik, mohamed azouazi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License