Application of LVQ Neural Network in Real–Time Adaptive Traffic Signal Control

Abstract

Real–time road traffic data analysis is the cornerstone for the modern transport system. The real–time adaptive traffic signal control system is an essential part for the system. This analysis is to describe a traffic scene in a way similar to that of a human reporting the traffic status and the extraction of traffic parameters such as vehicle queue length, traffic volume, lane occupancy and speed measurement. This paper proposed the application of two–stage neural network in real–time adaptive traffic signal control system capable of analysing the traffic scene detected by video camera, processing the data, determining the traffic parameters and using the parameters to decide the control strategies. The two–stage neural network is used to process the traffic scene and decide the traffic control methods: optimum priority or optimum locality. Based on simulation in the traffic laboratory and field testing, the proposed control system is able to recognise the traffic pattern and enhance the traffic parameters thus easing traffic congestion more effectively than existing control systems. Key words: Urban traffic control system, pattern recognition, two-stage neural network, adaptive control system