Hybrid short-term traffic flow prediction model of intersections based on improved complete ensemble empirical mode decomposition with adaptive noise. (April 2019)

• Record Type: Journal Article
• Authors: Tian, Xiujuan; Yu, Dexin; Xing, Xue; Wang, Shiguang; Wang, Zhuorui
• Is Part Of: Advances in mechanical engineering. Volume 11:Number 4(2019)
• Publication Date: 2019-04
• Subjects: Short-term traffic flow prediction -- hybrid prediction model -- improved empirical mode decomposition -- kernel extreme learning machine; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814019841819
• Languages: English
• ISSNs: 1687-8132

Abstract

Based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm and kernel online sequential extreme learning machine (KOSELM) algorithm, a new hybrid short-term traffic flow prediction model (ICEEMDAN-KOSELM-ARIMA) for signalized intersections is proposed according to the current and historical traffic flow data. First, traffic flow historical time series are decomposed by ICEEMDAN algorithm for the purpose of improving the prediction accuracy. Several intrinsic mode functions could be obtained by the decomposition process. Then, permutation entropy algorithm is employed to analyze the random properties of intrinsic mode function components. According to the different random properties of intrinsic mode functions, different prediction models can be built. On this basis, KOSELM prediction models are established for the intrinsic mode function components with big randomness. And auto-regressive integrated moving average (ARIMA) prediction models are built for the intrinsic mode function components with small randomness. Finally, an actual signalized intersection is selected to verify the effect and performance of the hybrid prediction model proposed in this article. Results show that compared with other models, the new proposed hybrid prediction model can effectively improve prediction accuracy, of which prediction errors are the lowest and fitting effect with actual values is the best.