Abstract
This study develops three measures to optimize the junction-tree-based reinforcement learning (RL) algorithm, which will be used for network-wide signal coordination. The first measure is to optimize the frequency of running the junction-tree algorithm (JTA) and the intersection status division. The second one is to optimize the JTA information transmission mode. The third one is to optimize the operation of a single intersection. A test network and three test groups are built to analyze the optimization effect. Group 1 is the control group, group 2 adopts the optimizations for the basic parameters and the information transmission mode, and group 3 adopts optimizations for the operation of a single intersection. Environments with different congestion levels are also tested. Results show that optimizations of the basic parameters and the information transmission mode can improve the system efficiency and the flexibility of the green light, and optimizing the operation of a single intersection can improve the efficiency of both the system and the individual intersection. By applying the proposed optimizations to the existing JTA-based RL algorithm, network-wide signal coordination can perform better.
Highlights
Signal control system is an important method of improving the operation of urban traffic
Some work has included developments in the max-plus algorithm and junction-tree algorithm (JTA); these have been applied to signal coordination control research at the road network level
In the JTA-based reinforcement learning (RL) algorithm, the root and leaf nodes determine the direction of information transmission along the junction tree
Summary
Signal control system is an important method of improving the operation of urban traffic. The signal coordination control can achieve better effects than the single-point signal control and the inductive signal control, there are many restrictions on the signal coordination control, such as difficulty in parameter calibration, computational complexity, and poor adaptability and stability Considering these restrictions and the fact that the dynamic characteristics of the traffic environment provide the need for interactive environment-based learning from the environment, machine learning algorithms are proposed to be used in signal coordination control research. Some work has included developments in the max-plus algorithm and junction-tree algorithm (JTA); these have been applied to signal coordination control research at the road network level. Zhu et al [16] first proposed the JTA instead of the max-plus algorithm to obtain the best joint action for traffic signals and to realize network-wide signal coordination. (1) To optimize the basic parameters of the JTA algorithm so that the signal coordination control scheme is consistent with actual requirements (2) To evaluate the impact of existing algorithms on local intersection operations (3) To propose optimization measures for local intersections to improve the practical application value of the algorithm
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
