Research on optimized GA-SVM vehicle speed prediction model based on driver-vehicle-road-traffic system
| Autor: | Wanzhong Zhao, Yufang Li, Chen Mingnuo, XiaoDing Lu |
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| Rok vydání: | 2018 |
| Předmět: |
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050210 logistics & transportation Computer science Energy management 020209 energy 05 social sciences General Engineering 02 engineering and technology computer.software_genre Support vector machine Prediction algorithms Online optimization 0502 economics and business Traffic conditions 0202 electrical engineering electronic engineering information engineering In vehicle General Materials Science Data mining computer Road traffic |
| Zdroj: | Science China Technological Sciences. 61:782-790 |
| ISSN: | 1869-1900 1674-7321 |
| DOI: | 10.1007/s11431-017-9213-0 |
| Popis: | The accurate prediction of vehicle speed plays an important role in vehicle’s real-time energy management and online optimization control. However, the current forecast methods are mostly based on traffic conditions to predict the speed, while ignoring the impact of the driver-vehicle-road system on the actual speed profile. In this paper, the correlation of velocity and its effect factors under various driving conditions were firstly analyzed based on driver-vehicle-road-traffic data records for a more accurate prediction model. With the modeling time and prediction time considered separately, the effectiveness and accuracy of several typical artificial-intelligence speed prediction algorithms were analyzed. The results show that the combination of niche immunegenetic algorithm-support vector machine (NIGA-SVM) prediction algorithm on the city roads with genetic algorithm-support vector machine (GA-SVM) prediction algorithm on the suburb roads and on the freeway can sharply improve the accuracy and timeliness of vehicle speed forecasting. Afterwards, the optimized GA-SVM vehicle speed prediction model was established in accordance with the optimized GA-SVM prediction algorithm at different times. And the test results verified its validity and rationality of the prediction algorithm. |
| Databáze: | OpenAIRE |
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