Combined Prediction of Short-term Travel Time of Expressway Based on CEEMDAN Decomposition

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Jia, Xingli, Zhou, Wuxiao, Li, Shuangqing, Chen, Xingpeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; Autoregressive processes; CEEMDAN; Data models; Deep learning; Empirical analysis; Expressway; Feature extraction; Prediction algorithms; Prediction models; Predictive models; Recurrent neural network; Recurrent neural networks; Road traffic control; Roads & highways; Root-mean-square errors; Short term; Time series analysis; Travel time; Travel time prediction
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3205736

Travel time is the basis for emergency intelligent control and guidance in expressway networks. To realize its accurate prediction and improve the expressway service level during emergencies, this study uses the combined model to predict the short-term travel time of expressway sections based on the expressway gantry data of Sichuan Province. First, the travel time series is extracted using a data matching algorithm, and the double standard deviation-cyclic elimination (2SD-CE) algorithm is used to clean the data. Then, combined with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the travel time subsequence was extracted, and the frequency of the subsequence was divided by Sample entropy (SampEn) algorithm. Based on this, bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), and vanilla recurrent neural network (vanilla RNN) models are used to construct prediction combination model 1 (CM1) under the condition of a single feature. Subsequently, the CEEMDAN and empirical mode decomposition (EMD) algorithms were combined with the LSTM algorithm to obtain the combination models (CM2 and CM3) without frequency division. The example calculation and analysis show that under different time granularities (5 min, 10 min, and 15 min) and different highway sections, the combined model can integrate the advantages of all prediction models and has higher prediction accuracy and stability, among which the prediction effect of CM1 can reduce the prediction value of root mean squared error (RMSE) by 18.8~26.4%, 0.8~41%, 4.1~13.3%.