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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Research on optimization method for traffic signal control at intersections in smart cities based on adaptive

Jingya Wei1, Yongfeng Ju1

  • 1School of Electronic and Control Engineering, Chang'an University, Xi'an, Shaanxi, 710064, China.

Heliyon
|May 20, 2024
PubMed
Summary

This study introduces an adaptive artificial fish swarm algorithm to optimize traffic signal control in smart city traffic intersections (SCTI). The method effectively reduces average delay and traffic congestion.

Keywords:
Adaptive artificial fish school algorithmChaotic searchOptimization of signal light controlSmart cityTraffic intersections

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Area of Science:

  • Intelligent Transportation Systems
  • Optimization Algorithms
  • Urban Planning

Background:

  • Smart city transportation environments are complex, with traffic flow influenced by numerous factors.
  • Optimizing traffic intersection signal control is crucial for enhancing efficiency and mitigating congestion in growing urban areas.

Purpose of the Study:

  • To develop and evaluate an adaptive artificial fish swarm algorithm for optimizing traffic signal control in smart city traffic intersections (SCTI).
  • To minimize average vehicle delay and the average number of stops at SCTIs.

Main Methods:

  • Established traffic flow state equations for SCTIs.
  • Designed SCTI signal control parameters using minimum average delay and average number of stops as objective functions.
  • Developed an optimization model for SCTI signal control.
  • Applied a hybrid approach combining chaotic search theory and an adaptively improved artificial fish swarm algorithm to solve the model.

Main Results:

  • The proposed method achieved an average delay of 7.8 ms.
  • The average number of stops was reduced to 2.
  • Average travel time was recorded at 68.4 seconds.

Conclusions:

  • The adaptive artificial fish swarm algorithm demonstrates significant effectiveness in optimizing traffic signal control at smart city intersections.
  • This approach enhances traffic signal control efficiency and substantially reduces traffic congestion in urban environments.