Prediksi Kemacetan Lalu Lintas di Persimpangan Menggunakan Metode Random Forest
Keywords:
Traffic Congestion, Intersection, Random Forest, Prediction, Machine LearningAbstract
Traffic congestion at intersections is signifikan problem in urban areas that causes decreased transportation efficiency, increased air pollution and economic losses. The research data were obtained from the extraction of live CCTV video with main features including time, number of vehicles, average speed and congestion class (congestion, light congestion, and freeway). The research data were obtained from the extraction of live CCTV video with main features including time, number of vehicles, average speed and congestion class (congestion, light congestion, and freeway). The dataset was then saved in CSV format and subjected to preprocessing, model training, and evaluation. The results indicate that this model can form the basis for an intelligent traffic management system. This research contributes to traffic management at intersections and supports the development of artificial intelligence-based solutions to reduce congestion
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