Feed-forward and RTRL neural networks for the macroscopic traffic flow prediction and monitoring: the potential of each other

This paper is about traffic flow short term prediction and monitoring based on magnetic sensors measurements. For these purposes, the advantages and drawbacks of feed-forward and real time recurrent learning neural networks are investigated. Structures determination, weights initialization, networks...

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Bibliographic Details
Main Authors: Messai, N., Thomas, P., El Moudni, A., Leclercq, E., Druaux, F., Lefebvre, D.
Format: Conference Proceeding
Language:English
Subjects:
Equations
Feedforward neural networks
Feedforward systems
Feeds
Fluid flow measurement
Magnetic sensors
Monitoring
Neural networks
Telecommunication traffic
Traffic control
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Summary:This paper is about traffic flow short term prediction and monitoring based on magnetic sensors measurements. For these purposes, the advantages and drawbacks of feed-forward and real time recurrent learning neural networks are investigated. Structures determination, weights initialization, networks training and automatic incidents detection are discussed.
DOI:10.1109/ITSC.2003.1251948