Capitalising modern data sources for observing and modelling transport behaviour

Abstract

The increasing popularity of agent-based models leads to new requirements regarding the data used for modelling and calibration. On the one hand, more detailed and insightful models are necessary to represent behaviour at an individual level. On the other hand, calibration data needs to be more finely-grained. For both exercises, data has to be available at a very high level of spatial and temporal detail. Modern survey technologies offer the potential to obtain this data. However, they also bring along new challenges that have to be overcome to make them usable for real world applications. This paper discusses some of these challenges along with a presentation of the modern survey technologies that are currently researched and used most actively. The aim is to provide guidelines that help researcher and practitioners to find the appropriate data source depending on the purpose and design of their study and the subsequent modelling.

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