Working on real-world transportation modeling problems: examples at SNCF

Organized by CERMICS and LVMT, the Data Transitions seminars aim to make cutting-edge scientific research in the digital and data fields accessible to all. They are open to anyone with a curious mind, whether junior or senior, expert or novice.

The next seminar, “Working on real-world transportation modeling problems: examples at SNCF,” will be led by Clément Mantoux (R&D engineer, SNCF) on Thursday, December 11, at 11:45 a.m. (room F107, Coriolis Building).

Working on real-world transportation modeling problems: examples at SNCF

There is a rich literature on modeling transportation systems to further their understanding and optimize their capacity. This knowledge, however, only proves useful as far as the available data can get us to. In practice, hurdles such as data availability and quality or operational constraints often prevent us from achieving these goals. The complexity of railway operations is reflected in the data they produce, which is often difficult to model and visualize at scale. Careful project planning and algorithm design may help circumvent some of these issues, and leverage the versatility of modern machine learning and optimization techniques. This talk dives into examples of such projects developed at SNCF, which rely on large-scale data management systems to enhance passenger train delay prediction and travel time estimation for freight wagons.