Spatiotemporal traffic prediction with missing data: A self-imputation assisted prediction model

Traffic prediction based on real-world traffic data is a crucial task in Intelligent Transportation Systems (ITS). However, the issue of missing observations due to real-world disturbances undermines the robustness and accuracy of traffic prediction. This problem necessitates the development of a prediction model that integrates the imputation mechanism to be compatible with missing observations. This paper introduces ATTST, a self-imputation-assisted prediction model specifically designed to address the challenge of missing observations in the traffic prediction task. Unlike traditional approaches utilizing an additional supervised imputation model before prediction, the imputation unit in our model does not need the extra label for the missing observations. Our model employs a self-imputation unit to impute the missing observations by partially masking the observed data as the ground true labels. Thus, the self-imputation unit along with an encoder–decoder architecture and a graph evolving unit together directly predict future traffic data with multi-level missing observations. The effectiveness of ATTST is validated using several real-world traffic datasets, including speed and flow data, across various multi-step prediction scenarios with diverse missing observations. These validations demonstrate the model’s robustness and practical applicability in real-world traffic prediction tasks. The results show that ATTST can reliably predict traffic conditions even with incomplete data, making it a valuable tool for traffic management and planning.