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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
25.04.2023
DOI
https://doi.org/10.7307/ptt.v35i2.60
ISSUE
2023 (Vol 35), Issue 2
LICENSE
Copyright (c) 2024 Chuwei Zhao, Yi Zhao, Zhiqi Wang, Jianxiao Ma, Minghao Li
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Authors:Chuwei Zhao, Yi Zhao, Zhiqi Wang, Jianxiao Ma, Minghao Li

Abstract

Urban intertunnel weaving (UIW) section is a special type of weaving section, where various lane-changing behaviours occur. To gain insight into the lane-changing behaviour in the UIW section, in this paper we attempt to analyse the decision feature and model the behaviour from the lane-changing point selection perspective. Based on field-collected lane-changing trajectory data, the lane-changing behaviours are divided into four types. Random forest method is applied to analyse the influencing factors of choice of lane-changing point. Moreover, a support vector machine model is adopted to perform decision behaviour modelling. Results reveal that there are significant differences in the influencing factors for different lane-changing types and different positions in the UIW segment. The three most important factor types are object vehicle status, current-lane rear vehicle status and target-lane rear vehicle status. The precision of the choice of lane-changing point models is at least 82%. The proposed method could reveal the detailed features of the lane-changing point selection behaviour in the UIW section and also provide a feasible choice of lane-changing point model.

Keywords:urban intertunnel weaving section, choice of lane-changing point, random forest, support vector machine

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