My name is Jonas Jostmann. I am a PhD student at the KTH since September 2023. After I graduated with a master’s degree in information systems from the University of Muenster in Germany, I worked for two years as a Data Scientist for Predictive Maintenance at CLAAS. My current research is focused on building a digital twin for transportation in the region of Kista (Stockholm) specifically targeting traffic caused noise and CO2 emissions.

PhD Project: Digital Twin in Transportation

To reduce traffic caused greenhouse gas and noise emission inflicting great harm to a city’s population, a detailed understanding of the underlying dynamics is required to steer efficient policy decisions. There already exist a variety of macroscopic models approaching this problem, which are, however, often biased or not detailed enough for effective traffic emission handling. Microscopic models represent a more adequate solution for this problem. However, their usage is also associated with challenges regarding high quality up-to-time simulations. Moreover, a single platform combining real-time traffic data with high resolution air quality data is missing to compare the model predictions with the real-world state.

By constructing a digital twin, exemplarily for the region of Kista in Stockholm, these challenges should be addressed. The digital twin combines CO2 sensing data with detailed traffic monitoring and simulations.  My research in this project focuses on estimating multi-class, dynamic origin-destination (OD) vehicle flows in real-time by developing a novel machine learning-based approach utilizing computational graphs.