Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction

Understand

The prediction of urban vehicle flow and speed can greatly facilitate people's travel, and also can provide reasonable advice for the decision-making of relevant government departments.

• However, due to the spatial, temporal and hierarchy of vehicle flow and many influencing factors such as weather, it is difficult to prediction.
• Most of the existing research methods are to extract spatial structure information on the road network and extract time series information from the historical data.
• However, when extracting spatial features, these methods have higher time and space complexity, and incorporate a lot of noise.