6,728 research outputs found
Context Trees: Augmenting Geospatial Trajectories with Context
Exposing latent knowledge in geospatial trajectories has the potential to
provide a better understanding of the movements of individuals and groups.
Motivated by such a desire, this work presents the context tree, a new
hierarchical data structure that summarises the context behind user actions in
a single model. We propose a method for context tree construction that augments
geospatial trajectories with land usage data to identify such contexts. Through
evaluation of the construction method and analysis of the properties of
generated context trees, we demonstrate the foundation for understanding and
modelling behaviour afforded. Summarising user contexts into a single data
structure gives easy access to information that would otherwise remain latent,
providing the basis for better understanding and predicting the actions and
behaviours of individuals and groups. Finally, we also present a method for
pruning context trees, for use in applications where it is desirable to reduce
the size of the tree while retaining useful information
People don't use the shortest path
Most recent route choice models, following either Random Utility Maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. Such study could also help practitioners and researchers evaluate widely applied shortest path assumptions. This study aims at bridging the gap by evaluating morning commute routes followed by residents at the Twin Cities, Minnesota. Accurate GPS and GIS data were employed to reveal routes people utilized. Findings from this study could also provide guidance for future efforts in building better travel demand models.Rationality, travel behavior, transport geography, commuting, transportation networks
Discovering private trajectories using background information
Trajectories are spatio-temporal traces of moving objects which contain valuable information to be harvested by spatio-temporal data mining techniques. Applications like city traffic planning, identification of evacuation routes, trend detection, and many more can benefit from trajectory mining. However, the trajectories of individuals often contain private and sensitive information, so anyone who possess trajectory data must take special care when disclosing this data. Removing identifiers from trajectories before the release is not effective against linkage type attacks, and rich sources of background information make it even worse. An alternative is to apply transformation techniques to map the given set of trajectories into another set where the distances are preserved. This way, the actual trajectories are not released, but the distance information can still be used for data mining techniques such as clustering. In this paper, we show that an unknown private trajectory can be reconstructed using the available background information together with the mutual distances released for data mining purposes. The background knowledge is in the form of known trajectories and extra information such as the speed limit. We provide analytical results which bound the number of the known trajectories needed to reconstruct private trajectories. Experiments performed on real trajectory data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds
PRESS: A Novel Framework of Trajectory Compression in Road Networks
Location data becomes more and more important. In this paper, we focus on the
trajectory data, and propose a new framework, namely PRESS (Paralleled
Road-Network-Based Trajectory Compression), to effectively compress trajectory
data under road network constraints. Different from existing work, PRESS
proposes a novel representation for trajectories to separate the spatial
representation of a trajectory from the temporal representation, and proposes a
Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal
Compression (BTC) algorithm to compress the spatial and temporal information of
trajectories respectively. PRESS also supports common spatial-temporal queries
without fully decompressing the data. Through an extensive experimental study
on real trajectory dataset, PRESS significantly outperforms existing approaches
in terms of saving storage cost of trajectory data with bounded errors.Comment: 27 pages, 17 figure
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