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Found 2 papers, 1 papers with code
Potential destination discovery for low predictability individuals based on knowledge graph
We first construct a trip knowledge graph (TKG) to model the trip scenario by entities (e. g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction.
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference.
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