Engineering Traffic Prediction With Online Data Imputation: A Graph-Theoretic Perspective

Abstract

Accurate and timely prediction about the current and near-term future traffic conditions is one of the effective ways to relieve traffic congestion and improve the operation efficiency of road networks. However, the missing data problem is inevitable when collecting real-time traffic flow information due to lossy communication and detector malfunction, which significantly affects the accuracy of prediction. To address this problem, in this article, we propose an approach to exploit online traffic prediction considering missing data effects. Unlike most existing methods imputing all the missing data before traffic prediction, the proposed strategy takes the spatio-temporal relationships between missing data and observed data into account to optimize data imputation patterns, thereby improving the efficiency of online traffic prediction. Specifically, we first propose an analytical framework for online traffic prediction with missing data by extending the space-time autoregressive integrated moving average model to incorporate missing data effects. Then, based on the combined use of optimal cut and data imputation optimization, a graph-theoretic technique is presented to determine the imputed data with consideration of road network topology and missing data pattern. Finally, experiments are conducted based on two real-world datasets. Experimental results indicate the superiority of the proposed approach in accuracy and efficiency compared with existing strategies of traffic flow prediction with missing data, particularly under the circumstance of high data missing ratios in urban road networks.