This paper proposes a general framework for a lane changing execution model. In the longitudinal direction, a weighted average of accelerations is obtained for the lane-changers by using the original and expected new leaders as the leading vehicles. In the latitudinal direction, the model computes the lateral moving speed through the product of longitudinal moving speed and the tangent steering angle. A formulation is proposed for describing the relationship between the steering angle and the lateral moving rate of the lane-changers. Vehicle trajectory data from the NGSIM are used for calibrating the proposed model. Based on the results of model calibration, it is found that the maximal steering angle is negatively correlated with the initial moving speed at the beginning of lane changing. The exponential weight is found as a good assumption. The proposed model is consistent with the car following model in behavioral fundamentals, which makes it simple to use and flexible for transplantation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Lane Changing Execution Modeling Based on Car Following Theory


    Contributors:
    Wang, Hao (author) / Li, Ye (author) / Dong, Chang-yin (author) / Wang, Wei (author)

    Conference:

    15th COTA International Conference of Transportation Professionals ; 2015 ; Beijing, China


    Published in:

    CICTP 2015 ; 2651-2662


    Publication date :

    2015-07-13




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Modeling of decision-making behavior for discretionary lane-changing execution

    Jianqiang Nie / Jian Zhang / Wan, Xia et al. | IEEE | 2016


    Modeling and Analysis of Lateral Driver Behavior in Lane-Changing Execution

    Yang, Da / Zhu, Liling / Yang, Fei et al. | Transportation Research Record | 2019


    Modeling and Analysis of the Lane-Changing Execution in Longitudinal Direction

    Yang, Da / Zhu, Liling / Ran, Bin et al. | IEEE | 2016