Real-Time Traffic Prediction: A Novel Imputation Optimization Algorithm with Missing Data

Abstract

Real-time and accurate prediction about current and future traffic conditions is one of the effective ways to alleviate traffic problems. However, missing data problem is inevitable for various reasons when obtaining real-time traffic flow information. Incomplete traffic information may seriously affect the prediction accuracy. To address this problem, in this paper, we propose a method that predicts the traffic flow in real time under missing data. Considering the spatio-temporal characteristics of traffic flows and the spatial location of road segments, we first evaluate the importance of traffic flows using a spatio-temporal correlation function to analyze the correlations of traffic flows. Then, we present a PPCA-based minimum data imputation optimization (P-MDIO) algorithm to reduce computation time of data imputation. Finally, we utilize the complete traffic data and relevant road segments sequences to predict real-time traffic flows. The experimental data are obtained from the real-time traffic data collected by loop detectors in Taipei, Taiwan. Our experimental results show the performance and validity of the proposed approach, particularly in large-scale prediction.