基于前车历史动力协作的交通流建模与仿真

Traffic Flow Modeling and Simulation Based on Preceding-Vehicle Historical Dynamic Coordination

摘要: 在智能交通快速发展的背景下，自动驾驶车辆凭借较强的数据感知、存储与计算能力，为交通流稳定性优化提供了新的技术基础。在智能网联环境下，车辆不仅能够获取实时驾驶状态信息，还具备存储与利用历史运行数据的条件。现有研究多围绕实时状态信息开展协同控制，而对历史信息的系统化融合与稳定性机制分析仍有进一步拓展空间。基于此，本文提出了一种基于前车历史动力协作的汽车跟驰模型，该模型将前车历史动力信息纳入控制决策框架，通过滤波机制对加速度响应进行平滑处理，以抑制扰动传播。基于线性稳定性理论与数值仿真方法，对模型的稳定性特征进行了分析与验证。结果表明，在合理参数范围内，引入前车历史动力协作能够显著提升交通流整体稳定性，为交通流稳定性优化提供了一种新的微观控制思路。

Abstract: With the rapid development of intelligent transportation systems (ITS), connected and automated vehicles (CAVs), equipped with advanced sensing, storage, and computational capabilities, provide a new technological foundation for traffic flow stability optimization. In intelligent connected environments, vehicles are able not only to acquire real-time driving state information but also to store and utilize historical operational data. Existing studies predominantly focus on cooperative control based on real-time state information, while the systematic integration of historical information and its underlying stability mechanisms remain insufficiently explored. To address this issue, this paper proposes a car-following model incorporating preceding-vehicle historical dynamic coordination. The proposed model integrates the historical dynamic information of the preceding vehicle into the control framework and applies a filtering mechanism to smooth acceleration responses, thereby suppressing disturbance propagation. Linear stability analysis and numerical simulations are conducted to examine the stability characteristics of the model. The results demonstrate that, within appropriate parameter ranges, incorporating preceding-vehicle historical dynamic coordination significantly enhances overall traffic flow stability, providing a novel microscopic control perspective for traffic flow stability optimization.