基于相关向量机和模糊综合评价的路况预测模型
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林浩,李雷孝,王慧,马志强,万剑雄
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Model based on relevance vector machine and fuzzy comprehensive evaluation for road condition prediction
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Hao LIN,Lei-xiao LI,Hui WANG,Zhi-qiang MA,Jian-xiong WAN
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表 5 不同算法模型预测车速的对比 |
Tab.5 Comparison of speed predicted by different algorithms and models |
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算法模型 | 数据集1 | | 数据集2 | | 数据集3 | MSE | RMSE | MAPE | MSE | RMSE | MAPE | MSE | RMSE | MAPE | PSO-SVM | 6.0762 | 2.4650 | 0.0738 | | 7.2539 | 2.6933 | 0.0768 | | 7.1888 | 2.6812 | 0.0754 | LSTM | 5.1940 | 2.2790 | 0.0596 | 5.6122 | 2.3690 | 0.0625 | 5.5984 | 2.3661 | 0.0629 | GRU | 5.1532 | 2.2701 | 0.0583 | 5.6074 | 2.3681 | 0.0628 | 5.6164 | 2.3699 | 0.0632 | CNN-LSTM | 5.1726 | 2.2740 | 0.0582 | 5.3024 | 2.3027 | 0.0613 | 5.5418 | 2.3541 | 0.0622 | CNN-GRU | 5.0687 | 2.2514 | 0.0576 | 5.2964 | 2.3014 | 0.0612 | 5.5469 | 2.3552 | 0.0621 | Bi-LSTM | 5.1557 | 2.2706 | 0.0579 | 5.4228 | 2.3287 | 0.0627 | 5.5691 | 2.3599 | 0.0625 | GA-CKRVM | 4.8118 | 2.1936 | 0.0581 | 5.0895 | 2.2560 | 0.0603 | 5.5145 | 2.3483 | 0.0597 | CNN-Bi-LSTM | 5.0794 | 2.2538 | 0.0572 | 5.1734 | 2.2745 | 0.0599 | 5.5286 | 2.3513 | 0.0603 | SPGAPSO-CKRVM | 4.6656 | 2.1601 | 0.0570 | 4.9809 | 2.2318 | 0.0586 | 5.4214 | 2.3284 | 0.0589 |
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