A Sigmoid-based car-following model to improve acceleration stability in traffic oscillation and following failure in free flow

TL;DR

This paper introduces Sigmoid-IDM, an improved car-following model that enhances acceleration stability and traffic flow safety by using a Sigmoid function and additional cautious driving features.

Contribution

The paper presents a novel Sigmoid-IDM model that improves traffic oscillation stability and following behavior over existing models.

Findings

Enhanced steady-state velocity in free flow

Improved local and string stability

Better anti-interference performance

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.