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Erschienen in:
2017 | OriginalPaper | Buchkapitel
A Unified Framework to Predict Movement
verfasst von : Olga Gkountouna, Dieter Pfoser, Carola Wenk, Andreas Züfle
Erschienen in: Advances in Spatial and Temporal Databases
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Abstract
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.