Abstract: Traffic forecasting is an important technology for building intelligent transportation systems. Real-time and accurate traffic forecasting is beneficial to route planning and improve travel efficiency. In order to improve the accuracy of traffic speed prediction, the article proposes a short-term traffic speed prediction model based on graph convolutional network. Firstly, the spatial and temporal characteristics of the traffic speed data are analyzed, and then the learnable adjacency matrix is constructed in combination with the data space characteristics to establish the graph convolution network. At the same time, considering the time characteristics of the traffic data, the long-term and short-term memory network and attention mechanism are added on the basis of graph convolution to jointly construct the prediction model. The experimental results show that due to the consideration of the temporal and spatial characteristics of traffic speed data, the root mean square error, average absolute error and average absolute percentage error of this model are all smaller than the traditional model and the single model, which verifies that the proposed model has higher prediction accuracy.

Key words: intelligent transportation, traffic speed prediction, time and space analysis, graph convolutional network, long-term and short-term memory network, attention mechanism