Predicting traffic breakdown on expressways using linear combination of vehicle detector data

抄録

Traffic congestion is closely associated with various social issues that must be solved urgently. With the recent advancement of machine learning technologies, diverse methods for predicting traffic congestion have been developed. Specifically, traffic prediction using deep learning can provide highly accurate performance. Nevertheless, several difficulties remain because of the complexity of deep learning models: particularly, they require large amounts of data and computational power. For this study, we strive to achieve traffic prediction precision using a simple linear model. Instead of improving complex models, we select training data appropriately with a linear model and then verify the feasibility of prediction by exploring “data complexity”. The prediction results imply that the linear model is as precise as deep learning even with fewer number of data and parameters. We use actual data from expressways collected using detectors.