B. Heilmann, N. El Faouzi, O. De Mouzon, N. Hainitz, H. Koller, D. Bauer, C. Antoniou:
"Predicting Motorway Traffic Performance By Data Fusion Of Local Sensor Data And Electronic Toll Collection Data";
Computer-Aided Civil And Infrastructure Engineering, Special Issue on Data Fusion and Data Assimilation (2011), 26; S. 451 - 463.

This paper proposes data fusion from different
sources in order to improve estimation and
prediction accuracy of traffic states on
motorways. This is demonstrated in two case
studies on an intra-urban and an inter-urban
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 four
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 min to two 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 centre.

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