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An integrated space-time pattern classification approach for individuals' travel trajectories
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Disaggregate travel data is not new to urban transportation planning studies, but it is infrequently handled in a GIS environment. With the evolution of urban travel demand models from aggregate models to disaggregate models and from a trip-based paradigm to an activity-based paradigm, there is a growing need of managing disaggregate travel data with spatial and temporal components in a GIS environment. At the data organization level, the main challenge is to efficiently store the data by minimizing redundancy while maintaining the complex relationships among the data items. The data organization should allow users to retrieve and visualize disaggregate travel data according to various possible combinations of spatial, temporal, and attribute criteria. This paper presents an implementation that employs a relational database approach and dynamic segmentation to organize the spatial, temporal, and attribute components in a sample travel diary data set. Discussions of the benefits and shortcomings associated with this approach are provided, along with suggestions for future research.