Toward Intelligent Variable Message Signs in Freeway Work Zones: Neural Network Model

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.