Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78082
Title: Decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena
Authors: Chen, J.
Low, K.H. 
Tan, C.K.-Y. 
Oran, A.
Jaillet, P.
Dolan, J.
Sukhatme, G.
Issue Date: 2012
Citation: Chen, J.,Low, K.H.,Tan, C.K.-Y.,Oran, A.,Jaillet, P.,Dolan, J.,Sukhatme, G. (2012). Decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena. Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012 : 163-173. ScholarBank@NUS Repository.
Abstract: 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 (D2FAS) algorithm for mobile sensors to actively explore the road network to gather and assimilate the most informative data for predicting the traffic phenomenon. We analyze the time and communication complexity of D2FAS and demonstrate that it can scale well with a large number of observations and sensors. We provide a theoretical guarantee on its predictive performance to be equivalent to that of a sophisticated centralized sparse approximation for the Gaussian process (GP) model: The computation of such a sparse approximate GP model can thus be parallelized and distributed among the mobile sensors (in a Google-like MapReduce paradigm), thereby achieving efficient and scalable prediction. We also theoretically guarantee its active sensing performance that improves under various practical environmental conditions. Empirical evaluation on real-world urban road network data shows that our D2FAS algorithm is significantly more time-efficient and scalable than state-ofthe- art centralized algorithms while achieving comparable predictive performance.
Source Title: Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/78082
ISBN: 9780974903989
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

53
checked on Oct 5, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.