A bagging ensemble learning traffic demand prediction model based on improved LSTM and transformer

Abstract

The paper aims to build a traffic prediction model for online car-hailing demand with Improved LSTM and Transformer. There are many factors that affect demand, such as temporal features, spatial features, high signal-to-noise ratio, and so on. In this study, LSTM and Transformer are used to extract the temporal and spatial features of data. The temporal and spatial features are used for bagging ensemble learning to predict the online car-hailing orders. An improved LSTM suitable for Traffic data sets is proposed in this paper. In LSTM, we use wavelet decomposition and reconstruction to extract the small-amplitude high-frequency signal from data. The extracted signal is translated and superimposed into the results of the model. The average MAE of 21.24 is obtained on Online Car-hailing Information Data Sets. Compared with other methods, the results suggest the proposed traffic demand prediction model has better accuracy.