Real-time traffic signal learning control using BPNN based on predictions of the probabilistic distribution of standing vehicles

Authors:
Chengyou Cui;Jisun Shin;Heehyol Lee
Affiliations:
Graduate School of Information, Production and Systems, Waseda University, Kitakyusyu, Japan 808-0135;Graduate School of Information, Production and Systems, Waseda University, Kitakyusyu, Japan 808-0135;Graduate School of Information, Production and Systems, Waseda University, Kitakyusyu, Japan 808-0135
Venue:
Artificial Life and Robotics
Year:
2010

Citing 3
Cited 0

Neural network design

Neural network design
Learning Bayesian Networks

Learning Bayesian Networks
Neural Networks for Real-Time Traffic Signal Control

IEEE Transactions on Intelligent Transportation Systems

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Abstract

In this article, a new method to predict the probabilistic distribution of a traffic jam at crossroads and a traffic signal learning control system are proposed. First, a dynamic Bayesian network is used to build a forecasting model to predict the probabilistic distribution of vehicles in a traffic jam during each period of the traffic signals. An adjusting algorithm for traffic signal control is applied to maintain the probability of a lower limit and a ceiling of standing vehicles to get the desired probabilistic distribution of standing vehicles. In order to achieve real-time control, a learning control system based on a back-propagation neural network is used. Finally, the effectiveness of the new traffic signal control system using actual traffic data will be shown.