Revealing the impact of stochastic driving characteristics on car-following behavior with locally collected vehicle trajectory data

Abstract

This study investigates the impact of Chinese drivers’ stochastic behaviour on local car-following situations using localized trajectory data. An extended stochastic car-following model (S-IDM) is established, which considers both internal and external stochasticity. External stochasticity is characterized by different driver types, while internal stochasticity is characterized by the standard deviation of acceleration under different headway and velocity differences for the same driver. The proposed model shows advantages in terms of single-vehicle simulation accuracy and traffic shock reproduction ability, compared to traditional and existing car-following models. The model can also be extended to the evolution analysis of mixed traffic flow models, where reducing the stochasticity of human-driven vehicles is critical for optimizing and controlling traffic flow.

KEYWORDS:

• Localized trajectory data
• car-following model
• stochastic behaviour
• traffic shock
• traffic simulation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China (grant number 52202408), China Postdoctoral Science Foundation (No. 2022M720719) and the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications (grant number NY222030).