Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network
This work addresses traffic prediction for intelligence transportation systems, offering an incremental improvement in efficiency for metropolitan scenarios.
The paper tackles metropolitan traffic prediction by proposing a Linkage Network model and Graph Recurrent Neural Network (GRNN) to reduce computational complexity from O(nm) to O(n+m) while maintaining high accuracy, as demonstrated on real-world data.
Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances. In this work, a new topological framework, called Linkage Network, is proposed to model the road networks and present the propagation patterns of traffic flow. Based on the Linkage Network model, a novel online predictor, named Graph Recurrent Neural Network (GRNN), is designed to learn the propagation patterns in the graph. It could simultaneously predict traffic flow for all road segments based on the information gathered from the whole graph, which thus reduces the computational complexity significantly from O(nm) to O(n+m), while keeping the high accuracy. Moreover, it can also predict the variations of traffic trends. Experiments based on real-world data demonstrate that the proposed method outperforms the existing prediction methods.