Open PhD Position - Transport MOde Detection and Analysis (MODA) (TSE 122)

Description of the Chair

The Chair of Transportation Systems Engineering (TSE) focuses on performing transportation research surrounding aspects of modelling and simulation of transportation systems, implementation of data science and data analytics in transport and human factors analysis, machine learning and deep learning. Specifically, the TSE chair performs research on both multimodal and unimodal freight and passenger transport demand and supply modelling, allowing for contributions on optimization, 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. Finally, the TSE chair contributes to the analysis of human factors in transport-related fields such as road safety modelling, behavioral economics applications and modelling of factors that affect transportation systems user engagement.

Position Description

Travel mode detection is a crucial task for transport demand management and understanding mobility patterns. Lack of data makes the mode detection task challenging to detect new transport modes and track trip chains. Thus, researchers and practitioners try to identify new data sources and plan optimal data collection infrastructure to develop advanced and robust models. In the MODA project, the successful candidate is going to tackle travel mode detection from both the data as well as the modeling side. We aim to design and implement a simulation test bed to collect (generate) mobility data covering new and traditional transport modes. We will design and test the opportunistic data collection framework to reflect the real-world challenges of data scarcity, sparsity, quality, and technical feasibility. Use of wavelet transformation and signal processing methods will be used to process and filter the data. We will propose and test state-of-the-art deep learning models to detect travel modes with high accuracy and robustness while addressing data quality issues. These models will be validated with respect to the simulation ground truth, as well as real-world data. Finally, models will be transferred to new contexts and study areas to tackle the lack of data.

The successful candidate will be able to contribute to the MODA project, which is an interdisciplinary project focusing on the development of data-driven models for mode classification using sparse, possibly opportunistic, data.

The PhD researcher will be supervised by Prof. Dr. Constantinos Antoniou (TUM) and cosupervised by Prof. Dr. Iuliia Yamnenko from National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute".



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

• Be enthusiastic about performing research on transport-related data analytics, and modeling, to learn new mathematical, analytic and simulation tools, new research approaches.

• Have strong analytical skills – e.g., data analytics, statistics, machine learning, and probability theories (probability distribution, Bayesian statistics, neural networks, spectral analysis).

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

• Like programming, and have an experience of 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.

Furthermore, candidates must fulfill the general TUM admission requirements:


Conditions of Employment

The successful candidate will be funded by a 3-year stipend from TUM IGSSE, within the research project MODA:


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 ([TSE122] and your name). Review of applications will begin immediately and continue until the position is filled.