measuring & modeling what we care about

Transportation challenges emerge from the way we shape and use our transportation systems. Only by holistically understanding the complex mobility system and the influence and interdependencies of its different components it is possible to develop valid models and assess the impact of specific transportation measures. This requires combining various methods - quantitative-statistic as well as qualitative-interpretative approaches – and the improvement of current transport simulations in order to identify successful influence measures

Investigating the complexity of mobility behavior requires careful selection of appropriate methods and their combination, including knowledge of their advantages and limitations (e.g. under-representation of hard-to-reach groups, distortion of results due to response bias or misinterpretations of observed behavior). In order to capture relevant data on mobility influence factors, we combine methods from different disciplines to extract major behavior-determinants, type-related characteristics (see IDENTIFY) and parameters which need to be implemented for improving current transport simulations, which insufficiently incorporate factors like incomplete information, emotional decisions, habits or social pressure.

Therefore, we use and develop cutting-edge methodologies for collecting data on external influence factors (e.g. infrastructure, weather, costs) as well as internal influence factors (e.g. preferences, habits, competences). The results are used for improving current transport models for more reliable assessment of the consequences of planned interventions and potential rebound effects of transportation measures (see e.g. project Smart City Rheintal).

Research activities and services:

  • Quantitative analysis of mobility determinants (surveys online and offline, stated preferences off – revealed preferences, assessing and handling methodological bias)
  • Qualitative analysis of mobility determinants (interviews: e.g. structured interview, narrative interview, focus group interview, participatory and non-participatory observation, cultural probing, assessing and handling methodological bias)
  • Development of human-centered analysis tools (e.g. computer-aided shadowing, virtual environment, gender-specific model of location-based quality of mobility options)
  • Spatial analysis and impact assessment (accessibility analysis, discrete choice modeling, stochastic route-choice modeling, macro- and mesoscopic transport modelling, microscopic traffic simulation)