Machine Learning for Enhanced Mobility

24.01.2020, 10:15-11:15  –  Campus Golm, Haus 28, Raum 0.108
SFB-Kolloquium

Katharina Morik (TU Dortmund)

Mobility is an important topic, because on the one hand, the global world requires mobility of people and goods, but on the other hand, we suffer from traffic jams and pollution. This broad field offers many applications for machine learning. If we can predict the traffic, then we could regulate it in several ways.

This talk presents an overview of a German study on car traffic, a German project on car mobility, a European project on traffic prediction in the case of disasters like, e.g., flood, another European project on multi-modal traffic prediction and focuses on spatio-temporal random fields as a resource efficient machine learning method that has been used for traffic prediction.

invited by Tobias Scheffer