Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Modeling Merging Acceleration and Deceleration Behavior Based on Gradient-Boosting Decision Tree
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.
Modeling Merging Acceleration and Deceleration Behavior Based on Gradient-Boosting Decision Tree
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.
Modeling Merging Acceleration and Deceleration Behavior Based on Gradient-Boosting Decision Tree
Li, Gen (Autor:in) / Fang, Song (Autor:in) / Ma, Jianxiao (Autor:in) / Cheng, Juan (Autor:in)
08.05.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Modeling vehicle acceleration-deceleration behavior during merge maneuvers
| Online Contents | 1997
Modeling vehicle acceleration-deceleration behavior during merge maneuvers
| British Library Online Contents | 1997
Modeling Vehicle Acceleration-Deceleration Behavior During Merge Maneuvers
| British Library Conference Proceedings | 1996