A macroscopic traffic flow model considering driver memory effect on curves

IF 4.6 2区物理与天体物理 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Results in Physics Pub Date : 2025-07-05 DOI:10.1016/j.rinp.2025.108353

Yang Chunfeng , Yang Chenxiao , Zhang Yu , Qi Linyuan , Shi Longyu , Tan Lixiang , Tang Qiang

引用次数: 0

Abstract

In real-world driving environments, curved roads are a common scenario that drivers must consider. Driver behavior is also a key factor in the study of traffic systems. This study investigates how factors such as curvature radius and friction coefficient affect the stability of a memory-dependent vehicle-following model under curved road conditions. First, stability criteria for the model are derived using control theory. Subsequently, equilibrium solutions are obtained through bifurcation analysis from a nonlinear perspective. Numerical simulations demonstrate that the model effectively captures system dynamics on curved roads. The incorporation of driver memory effects adversely impacts system stability, and similar destabilizing trends emerge with increasing curvature radius and friction coefficient. Furthermore, numerical solutions exhibit strong consistency with analytical results.

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考虑驾驶员记忆效应的弯道宏观交通流模型

在现实驾驶环境中，弯道是驾驶员必须考虑的常见场景。驾驶员行为也是交通系统研究中的一个关键因素。本文研究了曲率半径和摩擦系数等因素对弯道条件下记忆依赖车辆跟随模型稳定性的影响。首先，利用控制理论推导了模型的稳定性判据。然后，从非线性的角度出发，通过分岔分析得到平衡解。数值仿真结果表明，该模型能有效地捕捉弯曲道路上的系统动力学特征。驾驶员记忆效应的加入对系统稳定性产生不利影响，并且随着曲率半径和摩擦系数的增加，出现类似的不稳定趋势。此外，数值解与解析结果具有较强的一致性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Results in Physics MATERIALS SCIENCE, MULTIDISCIPLINARYPHYSIC-PHYSICS, MULTIDISCIPLINARY

CiteScore

8.70

自引率

9.40%

发文量

754

审稿时长

50 days

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