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The research field of transportation demand forecasting has started to focus on disaggregate travel behavior and micro‐simulation models. To create data infrastructure, disaggregate trip surveys are conducted and large numbers of observations are collected. To efficiently exploit these surveys, the transfer of the individual trip data to a GIS must start with the development of a solid conceptual data model that fully captures the semantic richness of the application domain and emphasizes its spatio‐temporal properties. This paper presents a data modeling process that is based on a combination of complex system theory and the object‐oriented paradigm and produced an object‐oriented spatio‐temporal data model. Main domain entities are modeled as highly structured classes. They encapsulate a memory of their time bound connections and states. Observation data sets are sampled from the origin‐destination survey conducted in the Québec region in 1991. This survey incorporated street networks and activity places. The model was smoothly implemented into a proof‐of‐concept database prototype hosted by an object‐oriented GIS shell. The prototype offers a means to navigate through a nested hierarchy of objects, providing a description of an individual’s travel behavior over space and time. The objects have a solid conceptual basis and can meet the needs of scientific research such as hypothesis formulation, simulation, forecasting and induction.
Jiang et al. (Sat,) studied this question.