GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting
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 mo...
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MDPI AG
2023-08-01
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| Series: | Mathematics |
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| author | Wenguang Chai Yuexin Zheng Lin Tian Jing Qin Teng Zhou |
| author_facet | Wenguang Chai Yuexin Zheng Lin Tian Jing Qin Teng Zhou |
| author_sort | Wenguang Chai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:45:14Z |
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| id | doaj.art-17b7ac035bd94a869722f1d4fa5ea5f3 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T23:45:14Z |
| publishDate | 2023-08-01 |
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| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-17b7ac035bd94a869722f1d4fa5ea5f32023-11-19T02:04:01ZengMDPI AGMathematics2227-73902023-08-011116357410.3390/math11163574GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow ForecastingWenguang Chai0Yuexin Zheng1Lin Tian2Jing Qin3Teng Zhou4School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaDepartment of Electronics and Engineering, Yili Normal University, Yining 835000, ChinaCentre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong KongCentre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong KongA 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.https://www.mdpi.com/2227-7390/11/16/3574kernel extreme learning machineshort-term traffic flow forecastinggenetic algorithm |
| spellingShingle | Wenguang Chai Yuexin Zheng Lin Tian Jing Qin Teng Zhou GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting Mathematics kernel extreme learning machine short-term traffic flow forecasting genetic algorithm |
| title | GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting |
| title_full | GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting |
| title_fullStr | GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting |
| title_full_unstemmed | GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting |
| title_short | GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting |
| title_sort | ga kelm genetic algorithm improved kernel extreme learning machine for traffic flow forecasting |
| topic | kernel extreme learning machine short-term traffic flow forecasting genetic algorithm |
| url | https://www.mdpi.com/2227-7390/11/16/3574 |
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