Lane changes are critical maneuvers on roadways. The maneuvers affect the safety gap between vehicles and may adversely affect automated vehicle operations and safety. Though previous researchers have developed several models to capture lane change behavior, few studies have comprehensively analyzed lane change under natural settings. This paper presents an exploratory lane change model based on the Shanghai Naturalistic Driving Study (SH-NDS). Using 5,339 lane change events extracted from SH-NDS, a gap acceptance and a lane change duration model were developed to give a new insight into lane change decision-making and execution process. Results show that road type, weather, light conditions, relative speed, and surrounding vehicle’s acceleration are crucial factors that might influence drivers’ gap acceptance, though lane change duration is mainly affected by absolute speed and longitudinal gap between vehicles. This study can extend the exploration and development of lane change behavior and its applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modeling Lane Change Gap Acceptance and Duration Using Shanghai Naturalistic Driving Data


    Contributors:

    Conference:

    18th COTA International Conference of Transportation Professionals ; 2018 ; Beijing, China


    Published in:

    CICTP 2018 ; 1918-1927


    Publication date :

    2018-07-02




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Lane Change Detection Using Naturalistic Driving Data

    Guo, Hongyu / Xie, Kun / Keyvan-Ekbatani, Mehdi | IEEE | 2021


    Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

    Lanfang Zhang / Cheng Chen / Jiayan Zhang et al. | DOAJ | 2018

    Free access

    Driving Style Recognition Based on Lane Change Behavior Analysis Using Naturalistic Driving Data

    Gao, Zhen / Liang, Yongchao / Zheng, Jiangyu et al. | ASCE | 2020


    Deep Learning Approach for Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data

    Das, Anik / Khan, Md Nasim / Ahmed, Mohamed M. | Transportation Research Record | 2022


    Analysis of Driving Control Model of Normal Lane Change based on Naturalistic Driving Data

    Zhang, Jiarui / Ma, Zhixiong / Zhu, Xichan et al. | IEEE | 2019