Prediksi Perambatan Arus Lalu Lintas Berdasarkan Korelasi Tertinggi Antar Jalan - Bagus Priambodo; Yuwan Jumaryadi; Umniy Salamah (STMIK Budi Darma, 2022)
Over the past few years, many algorithms for traffic flow predictions have been proposed to predict traffic flow. Time series models and neural network models have been widely implemented to predict traffic flow and traffic congestion based on traffic data, vehicle speed, weather, accidents, and special days. However, most of previous studies are used to predict traffic flow, not applied to predict the propagation of traffic flow. Traffic flow propagation is an interesting study. Using this finding the driver can avoid neighboring roads affected by road congestion. We propose the correlation method to find the relationship between the road. To evaluate the relationship between the road, we display the correlation results in the map. The visualization show that the correlation of traffic when congestion occurs shows better in showing the relationship between the road than the correlation at all times