Daily travel behavior

Abstract

Multi-agent models for simulating the mobility behavior of the urban population are gaining momentum due to increasing computing power. Such models pose high demands in terms of input data in order to be reliably able to match real world behavior. To run the models a synthetic population mirroring typical mobility demand needs to be generated based on real world observations. Traditionally this is done using travel diary surveys, which are costly (and hence have relatively low sample size) and focus mainly on trip choice rather than on activities for an entire day. Thus in this setting the generation of synthetic populations either relies on resampling identical activity chains or on imposing independence of various trips occurring during the day. Both assumptions are not realistic.

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