Error-distribution-free kernel extreme learning machine for traffic flow forecasting

Wu, Keer; Xu, Chunxiao; Yan, Jingwen; Wang, Fei; Lin, Zhizhe; Zhou, Teng

doi:10.1016/j.engappai.2023.106411

Cited by 11 publications

(1 citation statement)

References 37 publications

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“…Wang et al [32] proposed improved fuzzy C-means (FCM)-based ELM and demonstrated its superior performance in traffic flow prediction. Compared with the backpropagation algorithm, the ELM algorithm has a superior generalization ability and learns faster [33]. Furthermore, because ELM is based on the principle of the least squares method, it circumvents certain problems of the gradient-based learning method, such as falling into the local minimum and how to select the learning rate.…”

Section: Introductionmentioning

confidence: 99%

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

Chai,

Zheng,

Tian

et al. 2023

Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%