STUDY OF REDUCING THE NUMBER OF TRAFFIC SENSORS IN THE ESTIMATION OF TRAFFIC FLOWS USING GRAPH NEURAL NETWORK - Putri, Adiyana
The increasing vehicle volume every year affects the prediction of the traffic system. The purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions due to connection disruptions or aging. To be able to estimate the data historical from the nearest sensor is needed. The available data is modeled in the simulator to obtain data on traffic flow, speed, and road density. In the case of Central Jakarta, several sensors work, and the others are not working. To estimate data on sensors that not working, models and scenarios are needed. In this study, graph neural network models and clustering scenarios were used.