Predicting Motorway Traffic Performance by Data Fusion of Local Sensor Data and Electronic Toll Collection Data

This article proposes data fusion from different sources to improve estimation and prediction accuracy of traffic states on motorways. This is demonstrated in two case studies on an intraurban and an interurban motorway section in Austria. Data fusion in this case combines local detector data and speed data from the Electronic Toll Collection (ETC) system for heavy goods vehicles (HGV). A macroscopic model for open motorway sections has been used to estimate passenger car and HGV density, applying a standard state-space model and a linear Kalman filter. The resulting historical database of 4 months of speed-density patterns has been used as a basis for pattern recognition. A nonparametric kernel predictor with memory length of 9 and 18 hours has been used to predict HGV speed for a prediction horizon of 15 minutes to 2 hours. Results show good overall prediction accuracy. Correlation analysis showed little bias of predicted speed for free flow and congested time intervals, whereas transition states between free flow and congestion were frequently biased. Prediction accuracy can be improved by applying a combination of different prediction methods. On the other hand, computational performance of the prediction has to be further improved prior to implementation in a traffic management center.