@article{ref1,
title="Toward intelligent variable message signs in freeway work zones: Neural network model",
journal="Journal of transportation engineering",
year="2004",
author="Hooshdar, Sina and Adeli, Hojjat",
volume="130",
number="1",
pages="83-93",
abstract="An increasingly popular method of managing freeway traffic is to use variable message signs (VMS). A neural network model is presented for real-time control of a VMS system in freeway work zones. The neural network is trained to detect the start of a queue in a work zone and provide a message in the freeway upstream. The travelers are informed about the congestion in a work zone when a queue starts to form. The intelligent VMS system can be trained with data for different periods within a day, such as morning and evening rush hours, nonrush hours during the day, and night, for a more detailed traffic flow prediction over the period of one day. Two different neural network training rules are used: the simple backpropagation (BP) and the Levenberg-Marquardt BP algorithms. The network is trained using data adapted from the measured data. Based on different numerical experiments it is observed that the convergence speed of the Levenberg-Marquardt BP algorithm is at least one order of magnitude faster than the simple BP algorithm for the work zone traffic queue detection problem.<p />",
language="",
issn="0733-947X",
doi="",
url="http://dx.doi.org/"
}