Car-following model considering the lane-changing prevention effect and its stability analysis

Jia, Bingmei; Yang, Da; Zhang, Xiaobo; Wu, Yuezhu; Guo, Qian

doi:10.1140/epjb/e2020-10028-3

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…In recent years, a large number of research upon car-following behavior for different aims, such as traffic flow prediction [20], feedback control [21] or the safety analysis [22,23], and based on various idea, such as considering driver' memory effect [24][25][26] or driver's visual characteristics [27], communication delay [28], reaction delay [29], have been worked out. Among them, several efforts upon vehicle platoon control in V2X environment [30,31] or the influence of V2X technology on driving behavior [32,33] have been conducted. However, understanding about the impact exerted by motion state information, which is available for the drivers using V2X technology in ITS, of preceding vehicles including ones in the adjacent lanes on car-following behavior and traffic flow is still limited.…”

Section: Introductionmentioning

confidence: 99%

An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment

et al. 2020

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Vehicle-to-everything (V2X) technology will significantly enhance the information perception ability of drivers and assist them in optimizing car-following behavior. Utilizing V2X technology, drivers could obtain motion state information of the front vehicle, non-neighboring front vehicle, and front vehicles in the adjacent lanes (these vehicles are collectively referred to as generalized preceding vehicles in this research). However, understanding of the impact exerted by the above information on car-following behavior and traffic flow is limited. In this paper, a car-following model considering the average velocity of generalized preceding vehicles (GPV) is proposed to explore the impact and then calibrated with the next generation simulation (NGSIM) data utilizing the genetic algorithm. The neutral stability condition of the model is derived via linear stability analysis. Numerical simulation on the starting, braking and disturbance propagation process is implemented to further study features of the established model and traffic flow stability. Research results suggest that the fitting accuracy of the GPV model is 40.497% higher than the full velocity difference (FVD) model. Good agreement between the theoretical analysis and the numerical simulation reveals that motion state information of GPV can stabilize traffic flow of following vehicles and thus alleviate traffic congestion.

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