Urban traffic signal timing optimization based on multi-layer chaos neural networks involving feedback

Urban traffic system is a complex system in a random way, it is necessary to optimize traffic control signals to cope with so many urban traffic problems. A multi-layer chaotic neural networks involving feedback (ML-CNN) was developed based on Hopfield networks and chaos theory, it was effectively used in dealing with the optimization of urban traffic signal timing. Also an energy function on the network and an equation on the average delay per vehicle for optimal computation were developed. Simulation research was carried out at the intersection in Jiangmen city in China, and which indicates that urban traffic signal timing's optimization by using ML-CNN could reduce 25.1% of the average delay per vehicle at intersection by using the conventional timing methods. The ML-CNN could also be used in other fields.