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The problem of modeling and predicting spatiotemporal traffic phenomena overan urban road network is important to many traffic applications such asdetecting and forecasting congestion hotspots. This paper presents adecentralized data fusion and active sensing (D2FAS) algorithm for mobilesensors to actively explore the road network to gather and assimilate the mostinformative data for predicting the traffic phenomenon. We analyze the time andcommunication complexity of D2FAS and demonstrate that it can scale well with alarge number of observations and sensors. We provide a theoretical guarantee onits predictive performance to be equivalent to that of a sophisticatedcentralized sparse approximation for the Gaussian process (GP) model: Thecomputation of such a sparse approximate GP model can thus be parallelized anddistributed among the mobile sensors (in a Google-like MapReduce paradigm),thereby achieving efficient and scalable prediction. We also theoreticallyguarantee its active sensing performance that improves under various practicalenvironmental conditions. Empirical evaluation on real-world urban road networkdata shows that our D2FAS algorithm is significantly more time-efficient andscalable than state-oftheart centralized algorithms while achieving comparablepredictive performance.

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