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Communication Dans Un Congrès Année : 2024

Exploring Subway Relational Model in Decision Support Systems

Sondoss Chtioui
  • Fonction : Auteur
  • PersonId : 1349428
Sébastien Saudrais
Sebti Mouelhi
Toufik Azib
Marc Ille
  • Fonction : Auteur
  • PersonId : 1349429
Mélanie Morel
  • Fonction : Auteur
  • PersonId : 1349430
Alexandre Rossi
  • Fonction : Auteur
Jerome Charmetant
  • Fonction : Auteur
  • PersonId : 1349431

Résumé

This paper examines recent research in the field of public transportation, specifically focusing on the development of learning algorithms for predicting the behavior of trains and buses. However, it underscores the overlooked significance of having a clear and structured representation of data entities. To address this oversight, a relational model is proposed that captures the essential data fields specific to the subway system to enhance the learning process. The model undergoes validation through collaboration with a metro control center and domain experts. Furthermore, the study integrates this relational model into a hybrid approach that combines online and offline machine learning techniques. This approach effectively forecasts delays and passenger flow, thereby enabling informed decision-making and optimizing rail operations through a decision support system. The paper concludes by emphasizing the pivotal role of the proposed model in facilitating the selection of relevant variables for each learning problem.
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Dates et versions

hal-04482433 , version 1 (29-02-2024)

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Sondoss Chtioui, Sébastien Saudrais, Sebti Mouelhi, Toufik Azib, Marc Ille, et al.. Exploring Subway Relational Model in Decision Support Systems. Proceedings of the 7th International Conference (ICITT), incorporating the 7th International Conference on Communication and Network Technology (ICCNT), Sep 2023, Madrid, Spain. pp.217 - 230, ⟨10.3233/atde240036⟩. ⟨hal-04482433⟩

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