AI-based traffic flow prediction

Abstract

In this paper we discuss an AI-based model for end-to-end traffic prediction tasks, which combines graph convolutional networks and gated recurrent units. The spatial feature of complex topologies and dynamic temporal features can be well extracted from spatial-temporal traffic data. Experiments with real-time traffic flow data sets show that this model has better performance compared to some baseline models.