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<p>Traffic congestion has a major impact on urban mobility, resulting in travel delays, fuel use, and emissions. Traffic Signal Control (TSC) is one of the main strategies to alleviate these issues. This systematic review using PRISMA combines 50 peer-reviewed articles from 2015 to 2025, addressing Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) methods applied to TSC optimization. Our review compares algorithmic performance, application contexts, and parameter adjustment strategies. Findings confirm that hybrid methods (e.g., Genetic Algorithms (GA) + Particle Swarm Optimization (PSO)) perform better than single algorithms, with up to a 28.9% decrease in average vehicle delay. PSO shows higher resilience for real-time usage, while GA provides robustness for offline, multi-objective planning. Parameter tuning plays an important role in improving performance, with the best GA mutation rates (0.01–0.1) and PSO inertia coefficients (~ 0.7) delivering the optimal results. The present review synthesizes current evidence into practical recommendations for researchers, transportation planners, and policymakers seeking to promote traffic management effectiveness and environmental sustainability.</p>

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A systematic review of evolutionary and swarm intelligence approaches for traffic signal control optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月19日

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  • Rishika Bhattacharyya,
  • Sumit Gupta,
  • Marisha,
  • Awadhesh Kumar,
  • Deepti Mishra,
  • Manjari Gupta

摘要

Traffic congestion has a major impact on urban mobility, resulting in travel delays, fuel use, and emissions. Traffic Signal Control (TSC) is one of the main strategies to alleviate these issues. This systematic review using PRISMA combines 50 peer-reviewed articles from 2015 to 2025, addressing Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) methods applied to TSC optimization. Our review compares algorithmic performance, application contexts, and parameter adjustment strategies. Findings confirm that hybrid methods (e.g., Genetic Algorithms (GA) + Particle Swarm Optimization (PSO)) perform better than single algorithms, with up to a 28.9% decrease in average vehicle delay. PSO shows higher resilience for real-time usage, while GA provides robustness for offline, multi-objective planning. Parameter tuning plays an important role in improving performance, with the best GA mutation rates (0.01–0.1) and PSO inertia coefficients (~ 0.7) delivering the optimal results. The present review synthesizes current evidence into practical recommendations for researchers, transportation planners, and policymakers seeking to promote traffic management effectiveness and environmental sustainability.