Fine-Grained Traffic Flow Prediction of Various Vehicle Types via Fusion of Multisource Data and Deep Learning Approaches

Abstract

Both road users and road administrators are keen to know traffic flow of fine-grained vehicle type. Successful prediction on the traffic flow of heavy, medium and small vehicle could contribute to the improvement of travel safety and efficiency. However, the classification on vehicle type is always not accurate enough using in practice. It could cost a lot to identify from the additional video cameras to cover the full-length of large-scale freeway with high-resolution to capture vehicles clearly. In this paper, empirical data are cleaned, normalized, compensated, filled, decoded and filtered with help of the fusion of vehicle detector data, remote microwave sensors data and toll collection data. The traffic flows of fine-grained heavy, medium and small vehicles are successfully reconstructed. Improved deep belief network (DBN) are then proposed to forecast traffic flow of different types of vehicles in 30-, 60- and 120-minutes time interval. Random-selected road segments on a ring way around a city are trained with data accumulated three months and predict data in the next month. According to prediction error analysis, the proposed method performs better in estimation and forecasting, with respect to the existing methods, especially for longer time prediction and heavy vehicle prediction. It would benefit traffic control to prevent freeway congestion escalation, protect the traffic infrastructure via heavy vehicle control, reduce the road risk, prompt quick emergency response and eventually contributes to more applications for intelligent transportation system (ITS).