Traffic Speed Sequence Prediction by Adaptive Weighted Long Short-Term Memory With Classification-Type Loss

Abstract

Accurate traffic speed prediction is necessary to promote the development of intelligent transportation systems. The construction of consummate models is challenging owing to nonlinearity, nonstationarity, and long-term dependence in traffic speed prediction. This study proposed an ensemble long short-term memory (LSTM) model that was based on adaptive weighting, in which ensemble learning was the main solution. First, a data preprocessing model based on a seasonal statistical model was introduced to reconcile the long- and short-term dependence of the data. Second, the LSTM time step was considered during training, and a classification-type loss was designed to calculate the error rate in the ensemble system. Last, an adaptive weighting strategy was constructed to integrate a series of LSTMs generated in the system to obtain an ensemble model for traffic speed prediction. The experimental results showed that the proposed method was more stable and accurate than individual methods and existing ensemble learning methods.