Log In Sign Up

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

by   Mingming Lu, et al.

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. It is difficult to apply on large graphs, and only considers the influence of surrounding connected road nodes on the central node, ignoring a very important hierarchical relationship, namely, similar information of similar node features and road network structures. In response to these problems, this paper proposes the Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) model. The model uses GCN (Graph Convolutional Networks) to extract spatial feature, GRU (Gated Recurrent Units) to extract temporal feature, and uses the learnable Pooling to extract hierarchical information, eliminate redundant information and reduce complexity. Applying this model to the vehicle flow and speed data of Shenzhen and Los Angeles has been well verified, and the time and memory consumption are effectively reduced under the compared precision.


Urban Traffic Flow Forecast Based on FastGCRNN

Traffic forecasting is an important prerequisite for the application of ...

Residual Graph Convolutional Recurrent Networks For Multi-step Traffic Flow Forecasting

Traffic flow forecasting is essential for traffic planning, control and ...

Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model

Accurate traffic conditions prediction provides a solid foundation for v...

Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks

Understanding on-road vehicle behaviour from a temporal sequence of sens...

A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing

Ridesharing has received global popularity due to its convenience and co...

Crime Prediction with Graph Neural Networks and Multivariate Normal Distributions

Existing approaches to the crime prediction problem are unsuccessful in ...