A Unified Framework to Predict Movement

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10411)


In the current data-centered era, there are many highly diverse data sources that provide information about movement on networks, such as GPS trajectories, traffic flow measurements, farecard data, pedestrian cameras, bike-share data and even geo-social movement trajectories. The challenge identified in this vision paper is to create a unified framework for aggregating and analyzing such diverse and uncertain movement data on networks. This requires probabilistic models to capture flow/volume and movement probabilities on a network over time. Novel algorithms are required to train these models from datasets with varying levels of uncertainty. By combining information from different networks, immediate applications of such a unifying movement model include optimal site planning, map construction, traffic management, and emergency management.



This research has been supported by National Science Foundation AitF grants CCF-1637576 and CCF-1637541.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.George Mason UniversityFairfaxUSA
  2. 2.Tulane UniversityNew OrleansUSA

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