Reference:
D. Sun,
A. Jamshidnejad, and
B. De Schutter,
"Adaptive parameterized control for coordinated traffic management
using reinforcement learning," Proceedings of the 22nd IFAC World
Congress, Yokohama, Japan, pp. 5463-5468, July 2023.
Abstract:
Traffic control is essential to reduce congestion in both urban and
freeway traffic networks. These control measures include ramp metering
and variable speed limits for freeways, and traffic signal control for
urban traffic. However, current traffic control methods are either too
simple to respond to complex traffic environment, or too sophisticated
for real-life implementation. In this paper, we propose an adaptive
parameterized control method for traffic management by using
reinforcement learning algorithms. This method takes advantage of the
simple structure of parameterized state-feedback controllers for
traffic; meanwhile, a reinforcement learning agent is employed to
adjust the parameters of the controllers on-line to react to the
varying environment. Therefore, the proposed method requires limited
real-time computational efforts, and is adaptive to external
disturbances. Furthermore, the reinforcement learning agent can
coordinate multiple local traffic controllers when adjusting their
parameters. The method is validated by a numerical case study on a
freeway network. Results show that the proposed method outperforms
conventional controllers when the system is exposed to a changing
environment.
Bibtex entry:
@inproceedings{SunJam:23-023,