Our movement analytics research project “AitF: Collaborative Research: Modeling movement on transportation networks using uncertain data”, 9/1/16 - 8/31/20, is supported by the National Science Foundation under awards NSF-CCF 1637576 (Tulane University) and NSF-CCF 1637541 (George Mason University) with a total grant amount of $825,533.

The objective of this project is to create a unified framework for aggregating and analyzing diverse and uncertain movement data on road networks, with the aim to provide tools for querying and predicting traffic volume and movement. Probabilistic movement models on the network will be developed that can handle heterogeneous data sources, including GPS trajectories, geo-tagged social media data, bike-share data, public transport data, and traffic volume data. The diversity and spatio-temporal uncertainty of this data will be addressed with a Bayesian traffic pattern learning approach that first trains the movement models with the more certain data, which in turn will be used to fill gaps in the more uncertain data. The project will advance the state-of-the-art in theoretical communities (computational geometry, data mining) as well as in applied communities (spatial databases, location science). The results of the research will available on the project website (movementanalytics.org), and will be disseminated in prestigious venues through presentations and demonstrations at conferences, and through publications in journals.

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