GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting

Wenguang Chai, Yuexin Zheng, Lin Tian, Jing Qin and Teng Zhou ()
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Wenguang Chai: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Yuexin Zheng: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Lin Tian: Department of Electronics and Engineering, Yili Normal University, Yining 835000, China
Jing Qin: Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong
Teng Zhou: Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong

Mathematics, 2023, vol. 11, issue 16, 1-15

Abstract: A prompt and precise estimation of traffic conditions on the scale of a few minutes by analyzing past data is crucial for establishing an effective intelligent traffic management system. Nevertheless, because of the irregularity and nonlinear features of traffic flow data, developing a prediction model with excellent robustness poses a significant obstacle. Therefore, we propose genetic-search-algorithm-improved kernel extreme learning machine, termed GA-KELM, to unleash the potential of improved prediction accuracy and generalization performance. By substituting the inner product with a kernel function, the accuracy of short-term traffic flow forecasting using extreme learning machines is enhanced. The genetic algorithm evades manual traversal of all possible parameters in searching for the optimal solution. The prediction performance of GA-KELM is evaluated on eleven benchmark datasets and compared with several state-of-the-art models. There are four benchmark datasets from the A1, A2, A4, and A8 highways near the ring road of Amsterdam, and the others are D1, D2, D3, D4, D5, D6, and P, close to Heathrow airport on the M25 expressway. On A1, A2, A4, and A8, the RMSEs of the GA-KELM model are 284.67 vehs/h, 193.83 vehs/h, 220.89 vehs/h, and 163.02 vehs/h, respectively, while the MAPEs of the GA-KELM model are 11.67%, 9.83%, 11.31%, and 12.59%, respectively. The results illustrate that the GA-KELM model is obviously superior to state-of-the-art models.

Keywords: kernel extreme learning machine; short-term traffic flow forecasting; genetic algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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