Problem Statement

In order to understand the mobility of people in cities and regions and to estimate the effects of measures, travel demand models are used. Up to now, these models have represented the mobility of average days. In addition, models currently assume that the modeled persons have complete information about their available mobility options. Due to these simplifications, the influences of changing weather or improved knowledge through new information systems cannot be taken into account in traffic models so far. Thus, important aspects of travel behavior are not considered in travel demand models.

Objective

The aim of the project parts of DaKiMo worked on at the IfV is first of all to better understand the influence of environmental framework conditions such as weather and information on travel behavior. Based on this, these findings are to be integrated into the travel demand model mobiTopp of the Karlsruhe region (regiomove). The extended agent-based travel demand model will then react sensitively to changing conditions, such as weather and information. In the overall project, multi- and intermodal information systems will be improved based on the findings of the IfV. Users will be provided with mobility suggestions tailored to their situation and preferences.

Methods

In the project, existing data sources such as weather data and other information sources (e.g., use of public transportation or data from information systems) are first collected. The data is consolidated in a fusion server and transferred into a uniform data system. The IfV evaluates these with regard to the correlations between environmental conditions and travel behavior. Based on this, a separate survey is designed to identify and quantify further influencing factors. With these empirical results, the mobiTopp travel demand model of the Karlsruhe region is extended so that these influencing factors are taken into account in the model. Finally, various simulation calculations will be carried out to quantify the effects of changing weather conditions or information systems.