Open PhD Position - MItigating Transport-Related Air Pollution (MI-TRAP) project (TSE 125)

European Commission Horizon Europe Framework Programme

Description of the Chair

The Chair of Transportation Systems Engineering (TSE) undertakes research in the transportation field with a specific focus on the modeling and simulation of transportation systems, implementation of data science and data analytics in transport and human factors analysis, and machine learning and deep learning. The TSE chair researches multimodal and unimodal freight and passenger transport demand and supply modelling, allowing for contributions on optimisation, calibration, and validation of transport models. In this direction, the application of Big Data acquisition and analysis is examined as well as the use of Data-driven flexible models. The TSE chair also contributes to the analysis of human factors in transport-related fields such as road safety modelling, behavioural economics applications, and modelling of factors that affect transportation systems user engagement.

Project and Position Description

The transport sector is one of the most polluting globally, with the latest figures showing that it generates 24% of global CO2 emissions from fossil fuel combustion and consumes 28% of total global energy (International Energy Agency, 2020a,b; United Nations, 2021). Pollution from transport in the form of greenhouse gases and aerosols has two main adverse outcomes: (i) it contributes to climate forcing which results in a net warming of global temperatures, and (ii) it impacts the health of populations exposed to the pollutants. However, it is currently difficult to link and attribute the impact of transport pollution directly to these adverse outcomes. The aim of the MI-TRAP project is to use a bottom-up dose-response framework to quantify the impact of pollution from transport on environmental and epidemiological outcomes. The project will be delivered by a large consortium of academic and industry partners, led by the National Centre for Scientific Research (Demokritos) in Greece. Results will be generated across all transport modes for a number of case study cities in Europe including Copenhagen, Milan, and Zurich, among others. 

In the project, TUM is leading the creation of a real-time multimodal traffic management system that outputs near real-time information on traffic flows, which feeds into connected modules that calculate air pollutant concentrations and noise levels. The project's key challenge is generating real-time multimodal traffic flows from sparse data. We propose to use non-conventional data sources (dynamic traffic volumes, link speeds, traffic composition) from commercial providers. These data will be combined with open-source data and non-public licensed data from cities (if available). The collected data will be scaled up using state-of-the-art machine learning models to predict current and future traffic volumes and speeds. For data efficiency, we propose to apply transfer learning to apply pre-trained models from one case study city to another. The TUM team will also lead scenario modelling in the project. We propose to apply statistical learning techniques using the baseline air and noise pollution data generated for each case study city as training data. Various scenarios can be tested by altering input feature data such as traffic speeds and flows, vehicle composition, and land use data to generate new predictions of air pollution and noise levels for each scenario.

The PhD researcher will be supervised by Prof. Dr. Constantinos Antoniou at TUM. As this is a collaborative research project delivered by a consortium of academic and industry partners, the TUM PhD candidate will participate in regular joint meetings and formal workshops both virtually and in-person.


• Have an MSc degree in a relevant field (e.g., transportation engineering, data science, computer science).

• Be enthusiastic about researching transport-related projects and understand the fundamentals of transportation systems and modeling.

• Have strong analytical skills – A strong knowledge of the principles of data science, statistical learning, and simulation modeling will be important in developing and applying the quantitative methods that will be used to generate network-level traffic flow and speed data. 

• Have excellent research, academic writing, and presentation skills.

• Like programming and have experience using Python or R.

• Have excellent working knowledge (written and oral) of English (knowledge of German will be considered a plus); and

• Be able to work with strict deadlines.

In addition to the above, candidates must fulfill the general TUM admission requirements:

Conditions of Employment

TUM offers a competitive compensation package in accordance with the Public Sector Collective Agreement of Länder (TV-L). This position is a 100% TV-L 13 initially funded for three years (A13 a.Z. or E13, depending on the circumstances of the successful applicant). The position's expected start date is October 2023. More information on the offered wages can be found at

TUM is an equal opportunity employer. Qualified women are particularly encouraged to apply. Applicants with disabilities are treated with preference, given comparable qualifications.


Interested applicants who fit the requirements of the position are asked to send the following to

• A curriculum vitae

• Academic transcripts

• A motivation letter

• The names and contact information of three references

Please include the position ID in the email subject ([TSE125] and your name). Review of applications will begin immediately and continue until the position is filled. The MI-TRAP project has an official start date of January 2024.