Short-term traffic flow prediction based on hybrid decomposition optimization and deep extreme learning machine

Precise and robust short-term traffic flow forecasting is vital to reduce carbon emissions and alleviate traffic congestion. However, developing a traffic flow prediction model that is both precise and robust is exceedingly difficult, owing to the nonlinear and non-stationary characteristics of traffic flow. This study proposes a novel hybrid model (CPQDELM) in pursuit of this objective. This model attains satisfactory prediction performance by integrating the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), dung beetle optimization algorithm based on quantum inspiration and multistrategy improvement(QMDBO), and deep extreme learning machine (DELM). The proposed model employs CEEMDAN and PE for data decomposition and reorganization and then uses the QMDBO algorithm to optimize the DELM parameters. Experiments are conducted in this study to validate the efficacy of the QMDBO algorithm and the CPQDELM hybrid model, respectively. First, the effectiveness of the improvement strategy proposed in this study is verified by comparing the proposed QMDBO algorithm with six other algorithms through experiments on the CEC2021 test function. Secondly, using two real-world datasets, comparing the CPQDELM hybrid model and twelve baseline models. The results indicate that the model proposed in this study performs better than the current methodologies. The RMSE, MAE, and MAPE of the model proposed in this study are all decreased by 27.24%, 25.97%, and 33.56%, respectively, when compared to the conventional DELM, using the S321k51 dataset of mountain scenic highway as an example.