Traffic signals are essential to provide safe driving that allows all traffic flows to share road intersection. However, they decrease the traffic flow fluency because of the queuing delay at each road intersection. In order to improve the traffic efficiency all over the road network, Intelligent Traffic Light Scheduling ITLS algorithm has been proposed. In this work, we introduce an ITLS algorithm based on Genetic Algorithm GA merging with Machine Learning ML algorithm. This algorithm schedules the time phases of each traffic light according to each real-time traffic flow that intends to cross the road intersection, whilst considering next time phases of traffic flow at each intersection by ML. In order to get each next time phases of traffic flow, we use Linear Regression LR algorithm as ML algorithm. The introduced algorithm aims to increase traffic fluency by decreasing the total waiting delay of all traveling vehicles at each road intersection in the road network. We compare the performance of our algorithm with the unimproved one for different simulated data. Results shows that, our algorithm increases the traffic fluency and decreases the waiting delay by 21.5i¾?% compared with the unimproved one.
['Biao Zhao', 'Chi Zhang', 'Lichen Zhang']
Real-Time Traffic Light Scheduling Algorithm Based on Genetic Algorithm and Machine Learning