Citation

Li G, Fang S, Ma J, Cheng J. J. Transp. Eng. A: Systems 2020; 146(7): e05020005.

Copyright

(Copyright © 2020, American Society of Civil Engineers)

DOI

10.1061/JTEPBS.0000386

PMID

unavailable

Abstract

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.

Language: en

Keywords

Data-driven method; Gradient-boosting decision tree; Merging acceleration/deceleration behavior; Microscopic traffic simulation