The department aims to deepen the scientific understanding of mobility by incorporating findings from behavioral science and psychology, data-driven approaches, and advanced analytical methods. This is intended to contribute to the development of innovative and empirically grounded knowledge that supports the design of sustainable, efficient, and people-centered transportation systems.

Three central pillars are defined to guide the department’s scientific work. It is recognized that transportation supply and demand do not arise in isolation but rather as a function of spatial structures. As part of the department of Spatial Planning, the department therefore examines the reciprocal relationship between spatial structures, mobility behavior, and mobility psychology. The central question is how spatial developments influence mobility and, conversely, how transportation systems help shape spaces. The methods applied are designed to precisely analyze these complex interactions.

Shaping mobility means understanding mobility: We study how and why people travel—and what factors bring about lasting changes in their mobility. Through empirical research, we generate knowledge and strive to translate it into practice to support evidence-based decisions and effective measures.

Study of Mobility Behavior and Mobility Psychology

To gain a comprehensive understanding of mobility, it is essential to acquire in-depth knowledge of the perspectives of people as the central group of actors. The research area therefore focuses on analyzing travel behavior, decision-making processes, and the psychological determinants of mobility decisions. By examining attitudes, perceptions, habits, and behavioral responses to transportation policy measures and innovations, the aim is to explore how individuals interact with transportation systems and to what extent behavioral changes can support the promotion of sustainable mobility patterns.

Big Data in Mobility Research

The dynamic and continuous growth of digital data sources is fundamentally transforming mobility research. To this end, in addition to survey data, the research group utilizes other data sources such as sensor data, mobile phone data, GPS traces, and other novel mobility data streams to observe and analyze mobility with unprecedented spatial and temporal resolution. This data provides new insights into mobility dynamics, urban movement patterns, and system performance, thereby complementing traditional, survey-based approaches.

Artificial Intelligence and Quantitative Methods

The use of advanced analytical techniques is essential for generating meaningful insights into mobility from a wide variety of data sources. The department develops and applies state-of-the-art quantitative methods, including machine learning, generative artificial intelligence, traffic modeling, and agent-based simulation approaches. These methods enable researchers to model traffic demand, predict behavioral responses to policy measures, and support evidence-based mobility planning.