Passenger flow forecasting framework based on vision transformer and inpainting: Application to a public transport system - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Passenger flow forecasting framework based on vision transformer and inpainting: Application to a public transport system

Résumé

Short-term forecasting is one of the most important challenges in intelligent transport systems (ITS). The demand information is crucial for transport operators in order to anticipate and optimize their service level and for travelers to have robust information. In addition, a good predictor can contribute to system resilience by predicting disrupted situations. In most forecasting models, the data collectors are based on regular time series and single stations of the urban area. This study considers trains as data collectors, i.e., sensors. Thus, we are not limited to single location sensors. Moreover, we have to deal with irregular time series.

Domaines

Autre
Fichier principal
Vignette du fichier
doc00035533.pdf (1.06 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03908149 , version 1 (20-12-2022)

Identifiants

  • HAL Id : hal-03908149 , version 1

Citer

Thomas Bapaume, Etienne Come, Jérémy Roos, Mostafa Ameli, Latifa Oukhellou. Passenger flow forecasting framework based on vision transformer and inpainting: Application to a public transport system. TRISTAN XI, the 11th Triennial Symposium on Transportation Analysis, Jun 2022, Balaclava, France. 4p. ⟨hal-03908149⟩
16 Consultations
10 Téléchargements

Partager

Gmail Facebook X LinkedIn More