This paper proposes a neuro-fuzzy model based on Sugeno-type ANFIS for adaptive control of urban intersections. The model accounts for the state of not only the central intersection but also four adjacent intersections through a 20-dimensional input vector. Based on the input data, the model automatically computes and adjusts the green signal duration in real time. Compared to a fixed-time control scheme, the proposed system achieves reductions in queue length and driver waiting time at intersections. Additionally, the model demonstrated its ability to suppress congestion cascade effects, blocking overflow from one intersection to adjacent ones.
| Mualliflar | Jalelov, Rustem, Seytnazarova, Aygul |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-06-07 |
| Son | 2 |
| Betlar | 335-340 |
| Til | Rus |
neyro-noravshan tizim, ANFIS, svetofor boshqaruvi, transport oqimi,, adaptiv boshqaruv, downstream bandlik, tirbandlik indeksi, kaskadli tirbandlik, aqlli transport tizimlari, yashil signal davomiyligi, neuro-fuzzy system, ANFIS, traffic light control, traffic flow, adaptive control, downstream occupancy, congestion index, cascading congestion, intelligent transportation systems, green signal duration
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