Abstract
Traffic flow patterns are in general repeated on a daily or weekly basis. To improve the traffic conditions by using the inherent repeatability of traffic flow, a novel signal control strategy for urban networks was developed via iterative learning control (ILC) approach. Rigorous analysis shows that the proposed learning control method can guarantee the asymptotic convergence. The impacts of the ILC-based signal control strategy on the macroscopic fundamental diagram (MFD) were analyzed by simulations on a test road network. The results show that the proposed ILC strategy can evenly distribute the accumulation in the network and improve the network mobility.
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 callMore From: Physica A: Statistical Mechanics and its Applications
Disclaimer: 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.
