GSA-KELM-KF: A Hybrid Model for Short-Term Traffic Flow Forecasting

Wenguang Chai, Liangguang Zhang, Zhizhe Lin, Jinglin Zhou () and Teng Zhou
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Wenguang Chai: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Liangguang Zhang: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Zhizhe Lin: School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Jinglin Zhou: School of Computer Science, Fudan University, Shanghai 200433, China
Teng Zhou: State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550000, China

Mathematics, 2023, vol. 12, issue 1, 1-16

Abstract: Short-term traffic flow forecasting, an essential enabler for intelligent transportation systems, is a fundamental and challenging task for dramatically changing traffic flow over time. In this paper, we present a gravitational search optimized kernel extreme learning machine, named GSA-KELM, to avoid manually traversing all possible parameters to improve the potential performance. Furthermore, with the interference of heavy-tailed impulse noise, the performance of KELM may be seriously deteriorated. Based on the Kalman filter that cleverly combines observed data and estimated data to perform the closed-loop management of errors and limit the errors within a certain range, we propose a combined model, termed GSA-KELM-KF. The experimental results of two real-world datasets demonstrate that GSA-KELM-KF outperforms the state-of-the-art parametric and non-parametric models.

Keywords: traffic flow theory; extreme learning machine; Kalman filter (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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