Abstract: Traffic speed forecasting is the foundation of intelligent transportation system,which can ease traffic congestion,save public resources and improve people's quality of life.In real situations,the collected traffic speed data are usually missing,and most of the existing research results only consider the scenarios with relatively complete data.The paper focuses on the traffic speed data in the missing scenarios,captures the spatio-temporal correlation,and predicts the future traffic speed.In order to make full use of the spatio-temporal characteristics of traffic data,this study proposes a new deep learning-based traffic speed forecasting model.Firstly,a “recover-predict” algorithm is designed,which first uses a self-supervised learning method to enable the model to recover the missing data and then predict the traffic speed.Secondly,a contrastive learning method is introduced to make the feature representation of the speed time series more robust.Finally,the scenarios with different missing data rates are simulated,and experimental results show that the prediction accuracy of the proposed method outperform existing methods with various missing rates,and experiments are designed to analyze the comparative learning method and different recovery algorithms to prove the effectiveness of the proposed method.

Key words: Traffic speed forecasting, Recovery of missing data, Graph neural network, Contrastive learning, Deep learning