@proceedings {99204,
	title = {Swarm Intelligence Algorithms in Traffic Signal Control: A Critical Review of Particle Swarm, Bee Colony, and Ant Colony Approaches},
	journal = {the 3rd International Conference on Computer Science{\textquoteright}s Complex Systems and Their Applications (ICCSA 2024)},
	year = {2024},
	pages = {182-196},
	address = {Oum Bouaghi, Algeria },
	abstract = {This paper presents a critical review of Swarm Intelligence (SI) algorithms{\textendash}Particle Swarm Optimization (PSO), Bee Colony Optimization (BCO), and Ant Colony Optimization (ACO){\textendash}and their applications in traffic signal control (TSC). SI techniques, inspired by natural processes, have emerged as effective tools for optimizing traffic signal timings to improve urban traffic flow, reduce delays, and enhance overall efficiency. This review highlights the distinct strengths and limitations of each algorithm, with PSO excelling in simpler scenarios, BCO offering dynamic adaptability, and ACO providing robust path-finding capabilities. The paper also explores comparative analyses and hybrid models that combine these algorithms to leverage their complementary strengths. Despite significant advancements, challenges such as scalability, data dependency, and practical implementation remain. Future research directions include bridging the gap between theoretical and real-world applications, developing hybrid and adaptive models, and incorporating advanced data-driven techniques. The insights provided aim to guide further exploration and application of SI in creating more efficient and responsive traffic management systems.},
	url = {https://doi.org/10.1007/978-3-031-90758-6_14},
	author = {Romaissa Bendaikha and Djalal HEDJAZI}
}
