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Modeling Merging Acceleration and Deceleration Behavior Based on Gradient-Boosting Decision Tree
| 题名： | Modeling Merging Acceleration and Deceleration Behavior Based on Gradient-Boosting Decision Tree |
| 正文语种： | 英文 |
| 作者： | Gen Li;Song Fang;Jianxiao Ma;Juan Cheng |
| 作者单位： | College of Automobile and Traffic Engineering, Nanjing Forestry Univ;College of Automobile and Traffic Engineering, Nanjing Forestry Univ;School of Transportation, Southeast Univ |
| 摘要： | This paper aims to model the behavior of merging acceleration/deceleration when cars are running in a congested weaving section on a freeway during the merging implementation period by using a data-driven method called gradient-boosting decision tree (GBDT). Different from other black-box machine learning techniques, GBDT can provide abundant information about the nonlinear effects for independent variables by drawing the partial effects. Noise-filtered vehicle trajectory data collected on US Highway 101 are investigated in this study. The partial dependence plots show that the influence of independent variables on merging acceleration/deceleration is nonlinear and complicated and thus is different from the car-following behavior, which indicates that the adoption of traditional car-following models to merging execution behavior cannot reflect the distinctive behavior of merging vehicles. Evaluation of the performances in comparison with other state-of-the-art methods indicates that the proposed method can obtain more accurate results and thus is practical for simulating the merging execution behavior. |
| 出版日期： | 2020.01 |
| 出版年： | 2020 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | Vol.146 |
| 期： | No.07 |
| 页码： | 05020005 |
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