Hybrid short-term traffic flow prediction based on the effect of non-linear sequence noise

Short-term traffic flow prediction is crucial for intelligent transport systems and mitigating traffic congestion. Therefore, precise prediction of real-time traffic conditions is becoming more important. Currently, the existing prediction models lack the ability to effectively extract spatio-temporal characteristics and fail to adequately account for the impact of non-linear noise. To address these issues, the study proposes a hybrid short-term traffic flow prediction model based on spatio-temporal characteristics. First, the method decomposes the initial spatio-temporal traffic sequence data into multiple modal components using the complementary ensemble empirical modal decomposition method. Then, spatio-temporal characteristics are extracted from the decomposed spatio-temporal components using a deep residual network. The predicted values of each factor are combined to obtain the final predicted values. To validate the model, traffic flow data that is collected at point 4909A on the M25 motorway in London is used. The results indicate that the proposed model outperforms other models in terms of accuracy metrics such as root mean square error, mean absolute percentage error, mean absolute error, mean squared error, and coefficient of determination. Therefore, the model has high accuracy and practicality and exhibits great potential for short-term traffic flow prediction.