Abstract:

The current intelligent prediction methods for traffic flow have not analyzed and considered the spatiotemporal correlation of the road network. We conduct research and improvement to address this issue by adding spatiotemporal correlation information to the intelligent prediction methods to solve the problem of reduced prediction accuracy caused by the lack of spatiotemporal information. The spatiotemporal correlation of the urban road network is analyzed by combining the map connection of the traffic network and the vehicle traffic delay. Considering the spatiotemporal correlation of urban traffic, based on the GCN( Graph Convolutional Neural)

network and LSTM ( Long Short-Term Memory) network methods, the urban traffic flow prediction method considering spatiotemporal information based on GCN and LSTM is studied. Urban traffic flow prediction network is optimized and trained by using the open source urban traffic flow dataset. The performance of LSTM, BiLSTM (Bidirectional Long Short-Term Memory) network and different number of nodes in solving the traffic flow prediction problem is compared. The results of this research show that the proposed method can effectively predict urban traffic flow, and the accuracy of the proposed method is improved compared with the prediction method without considering spatiotemporal information. This research can provide a theoretical reference for traffic prediction in intelligent transportation systems.

Key words: graph convolutional neural network, short-term and short-term memory network, urban traffic, traffic flow prediction, spatiotemporal information