Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram

Xiaohui Lin; Jianmin Xu

doi:10.5604/01.3001.0014.1745

Authors

Xiaohui Lin Institute of Rail Traffic, Guangdong Communication Polytechnic, Guangzhou, China Author https://orcid.org/0000-0002-4126-5430
Jianmin Xu School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China Author https://orcid.org/0000-0002-8922-8815

DOI:

https://doi.org/10.5604/01.3001.0014.1745

Keywords:

multilayer boundary, traffic engineering, oversaturated intersection, macroscopic fundamental diagram, feedforward feedback, iterative control

Abstract

The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feed forward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.

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