Intelligent Transportation Design Based on Iterative Learning

Yinpu Ma, Kai Liu and Naeem Jan

Journal of Mathematics, 2022, vol. 2022, 1-7

Abstract: Most of the existing traffic optimization control methods are based on accurate mathematical models. As an uncertain and complex system, the urban traffic system faces difficulty in accurately calibrating the model parameters. Therefore, the existing methods become very difficult in the actual application process. Based on the massive data contained in the urban traffic system and the repetitive characteristics of traffic flow, this paper proposes a hierarchical traffic signal control method for urban road network based on iterative learning control. The simulation results show that the algorithm can achieve better control effect and can solve the problem of urban traffic congestion more effectively than traditional traffic control methods.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/jmath/2022/5027412.pdf (application/pdf)
http://downloads.hindawi.com/journals/jmath/2022/5027412.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:5027412

DOI: 10.1155/2022/5027412

Access Statistics for this article

More articles in Journal of Mathematics from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().