Real-time Traffic Flow Parameters Estimation Model Based on Generative Adversarial Network

To effectively allocate the spatio-temporal resources of a road network, it is necessary to estimate the traffic flow parameters in real time. The accurate estimation of traffic flow parameters requires the detailed consideration of the spatio-temporal characteristics of traffic flow in the road network. Based on the generative adversarial network, a real-time estimation model that can capture the spatio-temporal characteristics of traffic flow was formulated, that is, the TSTGAN model. This model included a generator and a discriminator. In the generator, the gated convolutional neural network was used to capture the dynamic spatial characteristics of traffic flow, and the long short-term memory neural network based on the attention mechanism was used to analyze the dynamic temporal characteristics of traffic flow. The discriminator consisted of the gated convolutional neural network and the long short-term memory neural network. The generator and discriminator in the generative adversarial network were trained by an adversarial mode, and the real-time estimated values of traffic flow parameters were obtained. The reliability of the TSTGAN model was validated using the traffic flow data obtained from 12 bayonet devices in Zibo City, Shandong Province, China, and 23 loop detectors in Los Angeles, California, America. The results show that: the introduced spatio-temporal block in the TSTGAN model can effectively extract the spatio-temporal characteristics of traffic flow, and the obtained root meansquare and mean absolute errors decrease by 2.12%~42.41% and 1.66%~40.49%, respectively, compared with those obtained from the existing models, which indicates that the formulated TSTGAN model can improve the estimation precision of traffic flow parameters. Copyright © 2022 by Science Press.