Data-Driven RBFNN-Enhanced Model-Free Adaptive Traffic Symmetrical Signal Control for a Multi-Phase Intersection with Fast-Changing Traffic Flow

Fast-changing demand in real traffic systems always leads to asymmetrical traffic flow and queues, which aggravates congestion and energy waste. In this paper, the traffic signal control problem of multi-phase intersections was studied with fast-changing traffic flows. First, a novel model-free adap...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 15; no. 6; p. 1235
Main Authors Ren, Ye, Yin, Hao, Wang, Li, Ji, Honghai
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Adaptive control
Algorithms
Analysis
artificial neural network
Cycling
data-driven control
model-free adaptive control
Multiphase
Neural networks
Queueing
Radial basis function
Traffic congestion
Traffic control
Traffic flow
Traffic intersections
Traffic models
traffic signal control
Traffic signals
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Summary:Fast-changing demand in real traffic systems always leads to asymmetrical traffic flow and queues, which aggravates congestion and energy waste. In this paper, the traffic signal control problem of multi-phase intersections was studied with fast-changing traffic flows. First, a novel model-free adaptive control-based symmetrical queuing balancing method was designed by using the full-format dynamic linearization (FFDL) technique. Second, in order to deal with the fast-changing traffic flow, a radial basis function neural network (RBFNN) was added to adjust parameters in a two-layer structure. Moreover, a variable cycle tuning algorithm was introduced to further reduce the time loss. Using the simulation, the proposed algorithm was compared with three other control strategies under low and high traffic demand, respectively, and the results showed the capability of the proposed algorithm.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym15061235