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[LOGO] Modeling Movement on Transportation Networks Using Uncertain Data

This page describes the scope and results funded by the following grant:
9/1/16 - 8/31/20 "AitF: Collaborative Research: Modeling movement on transportation networks using uncertain data", National Science Foundation, NSF-CCF 1637576, $317,681. Role: PI. Collaboration with Dieter Pfoser and Andreas Züfle at George Mason University; $825,533 total grant amount.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Project webpage: movementanalytics.org

Abstract

In the current data-centered era, there are many highly diverse data sources that provide information about movement on transportation networks. Examples include GPS trajectories, social media data, and traffic flow measurements. Much of this movement data is challenging to utilize due to the inherent uncertainty caused by infrequent sampling and sparse coverage. The goal of this project is to develop a unified framework that uses as many available data sources as possible to extract meaningful traffic and movement information automatically from the data. Probabilistic network movement models will be developed that capture movement probabilities and traffic volume on a network over time. The results will impact a range of applications that rely on capturing population movements, such as urban planning, geomarketing, traffic management, and emergency management. Educational activities will be integrated throughout the project. Students will be closely involved in research and practical implementations, and will be trained in spatio-temporal data management, algorithms development, and (trajectory) data analysis. The combination of such skills is increasingly important in spatial data science. Topics involved in this project will enrich the course material and curriculum development at both institutions. 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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Last modified by Carola Wenk,   cwenk  -at-   tulane  -dot-   edu , 08/26/2015 12:58:10