A NEURAL NETWORK APPROACH TO FREEWAY NETWORK TRAFFIC CONTROL

A NEURAL NETWORK APPROACH TO FREEWAY
NETWORK TRAFFIC CONTROL

M. Papageorgiou ¹⁾, A. Messmer ²⁾, J. Azema ³⁾ and D. Drewanz ³⁾

^{1) Dynamic Systems and Simulation Laboratory, Techn. Univ.
of Crete

²⁾ Ingenieurburo Dr. A. Messmer

³⁾ Lehrst. f. Steuerungs- und Regelungstechnik,
Techn. Univ. of Munich

Abstract

The paper investigates the application of
a feedforward neural network approach to freeway network control via variable direction
recommendations at bifurcation locations. A nonlinear control problem is formulated and
solved first by use of computationally expensive nonlinear optimization techniques. A
feedforward neural network is then trained by optimally adjusting its weights so as to
reproduce the optimal control law for a limited number of traffic scenarios.
Generalisation properties of the neural network are investigated and a discussion of
advantages and disadvantages compared with alternative control approaches is provided.

Key Words. Applied neural
control; backpropagation algorithms; conjugate gradient method; feedforward networks;
neural networks; optimal control; routing algorithms; traffic control.}