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

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Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena., , , , , , and . CoRR, (2014)Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena., , , , , , and . UAI, page 163-173. AUAI Press, (2012)An HMM-based map matching method with cumulative proximity-weight formulation., and . ICCVE, page 480-485. IEEE, (2013)Value-based control of the observation-decision process., , and . ACC, page 5041-5046. IEEE, (2008)Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network., , , , , , , , and . ITSC, page 983-988. IEEE, (2012)Location and Routing Models for Emergency Response Plans with Priorities., , , , and . Future Security, volume 318 of Communications in Computer and Information Science, page 129-140. Springer, (2012)Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents., , , and . IAT, page 478-485. IEEE Computer Society, (2012)978-1-4673-6057-9.Online map-matching based on Hidden Markov model for real-time traffic sensing applications., , , , , and . ITSC, page 776-781. IEEE, (2012)Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction., , , , , , , , and . IEEE Trans. Intell. Transp. Syst., 15 (2): 794-804 (2014)An Integrated Likelihood Formulation for Characterizing the Proximity of Position Measurements to Road Segments., and . IEEE Trans. Intell. Transp. Syst., 19 (6): 1839-1854 (2018)