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

Author

Messai, Nadhir ; Thomas, Philippe ; El Moudni, Abdellah ; Leclercq, Edouard ; Druaux, Fabrice ; Lefebvre, Dimitri

Author_Institution

UTBM, Belfort, France

Volume

1

fYear

2003

fDate

12-15 Oct. 2003

Firstpage

199

Abstract

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.

Keywords

computerised monitoring; feedforward neural nets; learning (artificial intelligence); magnetic sensors; recurrent neural nets; traffic engineering computing; RTRL neural networks; automatic incidents detection; feedforward neural networks; macroscopic traffic flow monitoring; macroscopic traffic flow prediction; magnetic sensor measurements; networks training; real time recurrent learning neural networks; structures determination; weights initialization; Equations; Feedforward neural networks; Feedforward systems; Feeds; Fluid flow measurement; Magnetic sensors; Monitoring; Neural networks; Telecommunication traffic; Traffic control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE

Print_ISBN

0-7803-8125-4

Type

conf

DOI

10.1109/ITSC.2003.1251948

Filename

1251948