Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

The transportation environment of smart cities is complex and ever-changing, and traffic flow is influenced by various factors. With the increase of traffic flow in smart cities, optimizing traffic intersection signal control has become an important method to improve traffic efficiency and reduce co...

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Bibliographic Details
Published in:Heliyon Vol. 10; no. 10; p. e30657
Main Authors: Wei, Jingya, Ju, Yongfeng
Format: Journal Article
Language:English
Published: England Elsevier Ltd 30.05.2024
Elsevier
Subjects:
Adaptive artificial fish school algorithm
algorithms
Chaotic search
equations
Optimization of signal light control
Smart city
system optimization
traffic
Traffic intersections
Traffic intersections
Chaotic search
Optimization of signal light control
Adaptive artificial fish school algorithm
Smart city
ISSN:2405-8440, 2405-8440
Online Access:Get full text
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Summary:The transportation environment of smart cities is complex and ever-changing, and traffic flow is influenced by various factors. With the increase of traffic flow in smart cities, optimizing traffic intersection signal control has become an important method to improve traffic efficiency and reduce congestion. To this end, a smart city traffic intersection(SCTI) signal control optimization method based on adaptive artificial fish swarm algorithm was studied. Establish the Equation of state of traffic flow at SCTIs to understand the actual traffic flow at SCTIs. On this basis, design SCTI signal control parameters, with the minimum average delay and average number of stops as objective functions, and construct an optimization model for SCTI signal control. By combining chaotic search theory and adaptively improving the artificial fish swarm algorithm, based on the adaptive artificial fish swarm algorithm, the intelligent city traffic intersection signal control optimization model is solved to achieve intelligent city traffic intersection signal control optimization. The experimental results show that the average delay of this method is 7.8 ms, the average number of stops is 2, and the travel time is 68.4 s s. Thus, it is proved that the method in this paper has a good optimization effect of traffic signal control at smart city intersections, which can improve the optimization efficiency of traffic signal control at smart city intersections and reduce traffic congestion at smart city intersections.
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e30657