A hybrid traffic controller system based on flower pollination algorithm and type-2 fuzzy logic optimized with crow search algorithm for signalized intersections

Korkmaz, Ersin; Akgüngör, Ali Payıdar · 2024 · Crossref

DOI: 10.1007/s00500-024-09643-w

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study addresses the challenge of optimizing traffic signal control at intersections to minimize vehicle delays and improve intersection performance amidst increasing urban traffic density. The authors developed a Hybrid Traffic Signal Control (HTSC) system that simultaneously optimizes signal phase plans, phase sequences, and signal timing durations. The motivation stems from the limitations of existing systems, which often optimize only timing or phase independently, and the need for dynamic controllers capable of handling the stochastic nature of traffic. The proposed system integrates the Flower Pollination Algorithm (FPA) for phase optimization and Type-2 Fuzzy Logic (T2FL) for time optimization, with the fuzzy logic parameters refined using the Crow Search Algorithm (CSA). The methodology employs a modular structure where the FPA determines the optimal phase plan and cycle length by minimizing delay per vehicle using the HCM 2000 delay formula, while prioritizing phases with higher vehicle counts. The T2FL module adjusts signal timing based on real-time traffic conditions, with its membership functions optimized by the CSA to enhance adaptability. The system was implemented and tested using a revised microscopic simulation program called KU-Trsim, which allows for detailed vehicle dynamics and signal control modeling. The performance of the HTSC was evaluated across nine different traffic conditions and four intersection geometries. It was compared against three baseline controllers: a fixed-time signal controller, a controller using only the FPA approach (FPA_TSC), and a controller using Type-1 fuzzy logic optimized with CSA (Type-1 FL-TSC). The results demonstrate that the HTSC system significantly outperforms the comparative methods. Specifically, the hybrid approach achieved approximately a 32% improvement in performance over the fixed-time controller. It also showed superior results compared to the single-algorithm approaches, performing 5% better than the FPA_TSC and 6% better than the Type-1 FL-TSC. The study highlights that combining phase and time optimization with advanced meta-heuristic algorithms and fuzzy logic effectively reduces vehicle delays, queue lengths, and driver dissatisfaction. The significance of this research lies in its contribution to intelligent transportation systems by providing a robust, adaptive control method for high-volume intersections. The HTSC system’s ability to dynamically adjust both phase plans and signal timings allows for more efficient traffic flow, which indirectly contributes to reduced fuel consumption and emissions. The study validates the effectiveness of integrating FPA and CSA with Type-2 fuzzy logic, offering a viable alternative to traditional and single-optimization AI-based traffic controllers. This approach provides a scalable solution for managing complex traffic uncertainties and improving overall intersection efficiency.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• adaptive driving beam