Large-Scale Network Imputation and Prediction of Traffic Volume Based on Multi-Source Data Collection System

Although newly developed traffic detectors were actively deployed to improve the accuracy and coverage of collecting city-wise traffic state information, the rapid transition of the traffic management system caused the problems of massive data corruption. For the practical application of recovering the missing values, the deep learning-based imputation technique is used, which relies on prediction performance with the consideration of dynamic spatial and temporal characteristics in the traffic state information. However, the existing method requires an assumption that the given data comprise a complete dataset from a single source based on the experiments evaluated on a small scale or long stream of freeways. In this paper, we propose a multi-variable spatio-temporal learning technique based on multi-source traffic state information, which was realized by adopting Attention-based Spatial–Temporal Graph Convolutional Networks (ASTGCN). The proposed imputation method cooperatively aggregates spatial and temporal correlation from two different types of detectors into an integrated framework, which allows us to predict missing volume regardless of the missing rate. Moreover, the study was conducted on a large-scale network that contains the entire road characteristics. Daejeon city has served as a case study to demonstrate the performance, and the results show that the mean absolute error of the proposed method is under 12 vehicles/5 min. Our work indicates that multi-source traffic state information can be utilized to impute city-wide missing traffic volume.