General architecture of an artificial intelligence–based traffic light system:(a case study of school no. 175, Mirobod District, Tashkent)
DOI:
https://doi.org/10.56143/2181-2438-2025-3-126-128Keywords:
AI-controlled traffic light, roadside schools, vehicular emissions, congestion, queue length, traffic flow optimization, fuel consumption, emission reductionAbstract
Traffic congestion during peak hours around schools negatively impacts transport efficiency, fuel consumption, and emission levels. At School No. 175 in the Mirobod district of Tashkent, an AI–PLC–based traffic light system was tested. The results demonstrated that delays were reduced by 65%, fuel consumption by 30–45%, and emissions by 40–50%.
Downloads
Published
2025-12-05
Issue
Section
Статьи
How to Cite
General architecture of an artificial intelligence–based traffic light system:(a case study of school no. 175, Mirobod District, Tashkent). (2025). Journal of Transport, 2(3), 126-128. https://doi.org/10.56143/2181-2438-2025-3-126-128
