Timeline: 2014-2017
Keywords: Prediction intervals; mobility data visualization; special events; disruptions
Team participants: Francisco C. Pereira, Filipe Rodrigues, Iñigo Reiriz, Niklas Pedersen, Evgheni Polisciuc
Lead Organization: DTU

Research highlights

• Prediction intervals for bus arrival times using quantile regression method and deep learning.
• Multi-modal visualization for extracting mobility insights.
• Anticipating mobility disruptions caused by special events.

Description

The TINV3 – Transportens Innovationsnetværk (Transport Innovation Network) helps to strengthen the Danish transport and logistics industry’s competitiveness and technological leadership. This research initiative financed a series of research projects in the MLSM group, covering topics such as prediction intervals in predictive models, mobility data visualization and the effects of special events (e.g. concerts, sports games, festivals, parades, etc.).

Sponsors and Partners

• TINV3 – Transportens Innovationsnetværk