A Forecasting Method of Dual Traffic Condition Indicators Based on Ensemble Learning

By the prediction of traffic conditions, the occurrence of traffic congestion can be warned in advance, so that the traffic managers can intervene in time, which can help to reduce the risk of traffic congestion. Therefore, aiming at the problem of traffic congestion, a prediction method for dual traffic condition indicators is proposed. The method for capturing spatial dependence based on the topology of roads and road driving direction is proposed to provide more flexible and targeted spatial features for predicting traffic conditions. In addition, according to the real-time and accuracy requirements of traffic conditions prediction, a novel model named dual-channel convolution block is designed to capture the temporal dependence of traffic conditions. Learning from the idea of ensemble learning, $K$ independent base models are trained to predict traffic condition at the same time, and a model fusion mechanism based on real-time traffic conditions is proposed to fuse the predictions of the base models so that the model can have stronger generalization ability to adapt to various noise data in real traffic conditions. The proposed method is validated on the traffic data sets and compares with the optimal model of all the existing models, the proposed method reduces MAPE of speed prediction by 12.1% and TTI prediction by 10.4%.