  Cooperative intelligent freeway traffic control is an important application
in intelligent transportation systems, which is expected to improve the
mobility of freeway networks. In this paper, we propose a deep neuroevolution
model, called ES-CTC, to achieve a cooperative control scheme of ramp metering,
differential variable speed limits and lane change control agents for improving
freeway traffic. In this model, the graph convolutional networks are used to
learn more meaningful spatial pattern from traffic sensors, a knowledge sharing
layer is designed for communication between different agents. The proposed
neural networks structure allows different agents share knowledge with each
other and execute action asynchronously. In order to address the delayed reward
and action asynchronism issues, the evolutionary strategy is utilized to train
the agents under stochastic traffic demands. The experimental results on a
simulated freeway section indicate that ES-CTC is a viable approach and
outperforms several existing methods
