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


  • Chuwei Zhao College of Automobile and Traffic Engineering, Nanjing Forestry University
  • Yi Zhao College of Automobile and Traffic Engineering, Nanjing Forestry University
  • Zhiqi Wang College of Automobile and Traffic Engineering, Nanjing Forestry University
  • Jianxiao Ma College of Automobile and Traffic Engineering, Nanjing Forestry University
  • Minghao Li College of Automobile and Traffic Engineering, Nanjing Forestry University



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


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.


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How to Cite

Zhao, C., Zhao, Y., Wang, Z., Ma, J., & Li, M. (2023). Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine. Promet - Traffic&Transportation, 35(2), 161–174.