A Sigmoid-based car-following model to improve acceleration stability in traffic oscillation and following failure in free flow
arXiv:2309.02456 · doi:10.1109/TITS.2024.3393490
Abstract
This paper proposes an improved Intelligent driving model (Sigmoid-IDM) to address the problems of excessive acceleration in traffic oscillation and following failure in free flow. The Sigmoid-IDM uses a Sigmoid function to enhance the start-following characteristics, improve the output strategy of the spacing term, and stabilize the steady-state velocity in free flow. Moreover, the model asymmetry is improved by means of introducing cautious following distance, driving caution factor, and segmentation function. The anti-interference ability of the Sigmoid-IDM is demonstrated by local stability and string stability analyses.
arXiv comment15 pages, 51 figures,
References in corpus (8)
- Congested Traffic States in Empirical Observations and Microscopic Simulations
- Generalized Force Model of Traffic Dynamics
- Enhanced Intelligent Driver Model to Access the Impact of Driving Strategies on Traffic Capacity
- Traffic experiment reveals the nature of car-following
- Estimating Acceleration and Lane-Changing Dynamics Based on NGSIM Trajectory Data
- Memory effects in microscopic traffic models and wide scattering in flow-density data
- Understanding widely scattered traffic flows, the capacity drop, platoons, and times-to-collision as effects of variance-driven time gaps
- A Generative Car-following Model Conditioned On Driving Styles