Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena

Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena

The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (D[superscript 2]FAS) algorithm for m...

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Bibliographic Details
Main Authors: Chen, Jie, Low, Kian Hsiang, Tan, Colin Keng-Yan, Oran, Ali, Jaillet, Patrick, Dolan, John, Sukhatme, Gaurav
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:en_US
Published: Association for Uncertainty in Artificial Intelligence (AUAI) 2014
Online Access:http://hdl.handle.net/1721.1/87049
https://orcid.org/0000-0002-8585-6566

Internet

http://hdl.handle.net/1721.1/87049
https://orcid.org/0000-0002-8585-6566

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