Machine learning methods for transportation under uncertainty

Timeline: 12/2018-12/2021
Keywords: Uncertainty, non-recurrent events, censored models, traffic prediction
Team participants: Inon Peled, Francisco Pereira, Kelvin Lee (NTU), Justin Dauwels (NTU)
Lead Organization: DTU – Technical University of Denmark

Description

The goal of the project is to develop and study effective modeling methods for Transportation under uncertainty scenarios. This is motivated by both the prevalence of uncertainty in Transportation and the widespread use of Transportation models in practice, e.g., for traffic management, planning of mobility services, and operation of Public Transport. We approach this goal through Machine Learning, namely, our proposed methods extract patterns from data and leverage them for better modeling.

Partners

DTU – Technical University of Denmark
Nanyang Technological University, Singapore

Sponsor

Ph.D. Alliances, NTU-DTU collaboration