Akademik Veri Yönetim Sistemi
Gazi University Journal of Science Part A: Engineering and Innovation, cilt.12, sa.1, ss.175-196, 2025 (TRDizin)
Traffic congestion in cities includes the complex and dangerous passing of emergency vehicles, whichis a time-consuming task. This problem requires the optimisation of traffic lights in favour of emergencyvehicles. To accomplish this, this paper discusses an optimized traffic light system using machinelearning that prioritizes the passing of emergency vehicles into city areas. It integrates SVM and RandomForest models by dynamically adjusting traffic light signals based on traffic density to accelerateemergency vehicles. The results reveal that the proposed system would lead to improved emergencyresponse times while enhancing overall transportation efficiency with reduced congestion of traffic.Additionally, the study further went on to establish the effectiveness of the proposed model as a solutionin traffic flow optimization and management. Results show that the performance of the proposed modelis effective for the purpose of traffic light optimization. The SVM+SAFS and RF+SAFS methodsfigured prominently as high-performance methods with accuracy rates of 94.89% and 95.02%,respectively. Furthermore, in the case of the RF+SAFS method used for traffic light optimization, it waspossible to reduce the average waiting time by 20%, increase the capacity of transit by 15%, and decreasefuel consumption by 10%. Overall, combining the outputs in the model led to the following performance,an 18% decrease in total travel time.