Short-term traffic volume prediction by ensemble learning in concept drifting environments

Abstract

Abstract Because of the rapid changes in traffic conditions caused by various circumstances, such as road construction and traffic jams, the distribution of the traffic volume data changes over time. The performances of traditional traffic volume prediction methods, with fixed model types and parameter settings, suffer from gradual degradation during these concept drift processes. In this paper, a novel incremental regression framework under the concept drifting environment is proposed, with ensemble learning as the major solution for updating the distribution representation. First, we transform the regression problem of traffic volume forecasting into a binary classification problem. Second, loss functions for incremental and ensemble learning are constructed based on this transformation. Finally, the incremental learning of the regression function is formulated as stepwise updating of the decision hyperplane. The experimental results show that our method is more stable and accurate than the existing incremental and ensemble regression methods.