A Hybrid ARIMA-LSTM Model for Short-Term Vehicle Speed Prediction

Wei Wang, Bin Ma, Xing Guo (), Yong Chen () and Yonghong Xu
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Wei Wang: School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
Bin Ma: School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
Xing Guo: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Yong Chen: School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
Yonghong Xu: School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China

Energies, 2024, vol. 17, issue 15, 1-18

Abstract: Short vehicle speed prediction is important in predictive energy management strategies, and the accuracy of the prediction is beneficial for energy-saving performance. However, the nonlinear feature of the speed series hinders the improvement of prediction accuracy. In this study, a novel hybrid model that combines an autoregressive integrated moving average (ARIMA) and a long short-term memory (LSTM) model is proposed to handle the nonlinear part efficiently. Generally, the ARIMA component filters out linear trends from the speed series data, and the parameters of the ARIMA are determined with the analysis. Then the LSTM handles the residual normalized nonlinear items, which is the residual of ARIMA. Finally, the two parts of the prediction results are superimposed to obtain the final speed prediction results. To assess the performance of the hybrid model (ARIMA-LSTM), two tested driving cycles and two typical driving scenarios are subjected to rigorous analysis. The results demonstrate that the combined prediction model outperforms individual methods ARIMA and LSTM in dealing with complex, nonlinear variations, and exhibits significantly improved performance metrics, including root mean square error ( RMSE ), mean absolute error ( MAE ), and mean percentage error ( MAPE ). The proposed hybrid model provides a further improvement for the accuracy prediction of vehicle traveling processes.

Keywords: speed prediction; hybrid model; ARIMA; LSTM; nonlinear (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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