Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling

Abstract

This study focuses on the problem of the supply prediction of logistics transportation vehicles in road transportation. Aiming at the problem that the supply data of logistics transportation has the characteristics of long sequential data, numerous influencing factors, and a significant spatiotemporal evolution law, which leads to the lack of accuracy of supply predictions, this paper proposes a supply prediction method for logistics transportation based on an improved Informer model. Firstly, multidimensional feature engineering is applied to historical supply data to enhance the interpretability of labeled data. Secondly, a spatiotemporal convolutional network is designed to extract the spatiotemporal features of the supply volume. Lastly, a long short-term memory (LSTM) model is introduced to capture the supply volume’s long- and short-term dependencies, and the predicted value is derived through a multilayer perceptron. The experimental results show that mean square error (MSE) is reduced by 73.8%, 79.36%, 82.24%, 78.58%, 77.02%, 53.96%, and 40.38%, and mean absolute error (MAE) is reduced by 52%, 59.5%, 60.36%, 57.52%, 53.9%, 31.21%, and 36.58%, respectively, when compared to the auto-regressive integrated moving average (ARIMA), support vector regression (SVR), LSTM, gated recurrent units (GRUs), a back propagation neural network (BPNN), and Informer and InformerStack single models; compared with the ARIMA + BPNN, ARIMA + GRU and ARIMA + LSTM integrated models, the MSE is reduced by 74.88%, 71.56%, and 74.07%, respectively, and the MAE is reduced by 51.31%, 50%, and 52.02%; it effectively reduces the supply prediction error and improves the prediction accuracy.

Keywords

• logistics transportation
• supply forecast
• informer model
• data feature engineering
• artificial neural networks