DocumentCode
2748708
Title
A neural network application in signal timing control
Author
Chin, Daniel C. ; Smith, Richard H.
Author_Institution
Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA
Volume
4
fYear
1996
fDate
3-6 Jun 1996
Firstpage
2101
Abstract
Generally the most cost-effective means of achieving optimized vehicle flow through a given road network is by improving the timing of traffic signals at network intersections. This paper uses neural networks (NN) as the bases for the control law. The NN weight estimation occurs real time in closed-loop mode via the simultaneous perturbation stochastic approximation algorithm. The approach results in a net 10-percent reduction in vehicle wait time over the performance of the existing, in-place strategy
Keywords
approximation theory; neural nets; road traffic; traffic control; closed-loop mode; network intersections; optimized vehicle flow; road network; signal timing control; simultaneous perturbation stochastic approximation algorithm; traffic signals; vehicle wait time; weight estimation; Automatic control; Centralized control; Communication system traffic control; Control systems; Intelligent networks; Neural networks; Roads; Timing; Traffic control; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1996., IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
0-7803-3210-5
Type
conf
DOI
10.1109/ICNN.1996.549226
Filename
549226
Link To Document