Discrete Continuous Travel Mode Choices based on Simulated Travel Demand: a MDCEV Model Application

The significantly higher level of detail of agent-based travel demand models (ABM) compared to aggregate models stands in contrast to their high simulation times. Once fast model responses are necessary, the application of ABMs may pose run time challenges. For such purposes, a condensation of ABMs’ sensitivities and saturation effects regarding travel mode choice is required, which - as shown in this paper - is achieved by a multiple discrete continuous extreme value model (MDCEV). The application case of this method is a serious game which enables decision-makers and citizens low-threshold access to a better understanding of interrelations within the urban mobility system. The mode choice model for the serious game is estimated on the basis of the simulated travel demand patterns of an ABM. The comparison of the model application with the ABM reference results attests a good ability to reproduce the current travel demand, even for different types of traffic. Also modal shift reactions on policy interventions reveal a high level of consistency. v