This paper presents a Bayesian hierarchical spatiotemporal method of interpolation, termed as Markov Cube Kriging (MCK). The classical Kriging methods become computationally prohibitive, especially for large datasets due to the O(n3) matrix decomposition. MCK offers novel and computationally efficient solutions to address spatiotemporal misalignment, mismatch in the spatiotemporal scales and mi...

Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication management network resource allocation for real-time application V2X network. Recurrent neural networks their variants have been reported in recent research to predict mobility. How...

Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic flow, air quality index to crime case, etc. Unlike the raster irregular and disordered characteristics have attracted research interest scholars, with prediction being one hot spots. The emergence neural networks (ST-GNNs) provides new insight for solving problem obtaining spatial correlation while achi...

Due to a variety of motions across different frames, it is highly challenging learn an effective spatiotemporal representation for accurate video saliency prediction (VSP). To address this issue, we develop feature alignment network tailored VSP, mainly including two key sub-networks: multi-scale deformable convolutional (MDAN) and bidirectional Long Short-Term Memory (Bi-ConvLSTM) network. The...