MobInFact_Englisch

The MobInFact research project, conducted by the IRAP Institute for Spatial Development in collaboration with two partner universities, examines the extent to which spatial and social factors influence our mobility. By participating in this online survey, you will help us lay the groundwork for targeted planning.

The modes of transportation we use in our daily lives are significantly influenced by the spatial conditions in our residential neighborhood and immediate surroundings, as well as by sociodemographic characteristics such as age or household size.

Among experts in urban and transportation planning, it is widely known which factors influence people’s mobility behavior and traffic volume. However, it remains unclear just how strong the influence of these factors is and how they interact.

Through this project (Mobility Influencing Factors), the IRAP Institute for Spatial Development is investigating these relationships and attempting to make them measurable.

Information about the research project

Objectives

The ultimate goal of the MobInFact project is to develop an easy-to-use toolbox that can be used to estimate the travel behavior of a neighborhood’s residents.

The tool is designed to help assess the impact of urban planning and transportation planning measures on mobility parameters (e.g., mode of transportation or distances traveled) with minimal effort

This makes it easier to understand the measures and their effects, and facilitates their evaluation for planning decisions. Scarce financial, human, and material resources can thus be deployed in a more targeted and effective manner.

Procedure

Development of evaluation methods to quantify key factors influencing mode choice.
Analysis and evaluation of the spatial and sociodemographic conditions of approximately 50 neighborhoods in Germany, Austria, and Switzerland, along with a simultaneous survey of the mobility behavior of the residents in these areas.
Identification of correlations between structural conditions and mobility behavior using statistical correlation analysis.
Development of a calculation tool based on the results obtained to estimate the effects of local conditions on mobility.
Consolidation of all components into a toolbox.