Xu Xing, Wu Zekun, Zhao Yun
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Sensors (Basel). 2024 Aug 11;24(16):5202. doi: 10.3390/s24165202.
Car-following models are crucial in adaptive cruise control systems, making them essential for developing intelligent transportation systems. This study investigates the characteristics of high-speed traffic flow by analyzing the relationship between headway distance and dynamic desired distance. Building upon the optimal velocity model theory, this paper proposes a novel traffic car-following computing system in the time domain by incorporating an absolutely safe time headway strategy and a relatively safe time headway strategy to adapt to the dynamic changes in high-speed traffic flow. The interpretable physical law of motion is used to compute and analyze the car-following behavior of the vehicle. Three different types of car-following behaviors are modeled, and the calculation relationship is optimized to reduce the number of parameters required in the model's adjustment. Furthermore, we improved the calculation of dynamic expected distance in the Intelligent Driver Model (IDM) to better suit actual road traffic conditions. The improved model was then calibrated through simulations that replicated changes in traffic flow. The calibration results demonstrate significant advantages of our new model in improving average traffic flow speed and vehicle speed stability. Compared to the classic car-following model IDM, our proposed model increases road capacity by 8.9%. These findings highlight its potential for widespread application within future intelligent transportation systems. This study optimizes the theoretical framework of car-following models and provides robust technical support for enhancing efficiency within high-speed transportation systems.
跟车模型在自适应巡航控制系统中至关重要，是发展智能交通系统必不可少的部分。本研究通过分析车头间距与动态期望间距之间的关系来探究高速交通流的特性。基于最优速度模型理论，本文提出了一种新颖的时域交通跟车计算系统，通过纳入绝对安全车头时距策略和相对安全车头时距策略，以适应高速交通流的动态变化。利用可解释的运动物理定律来计算和分析车辆的跟车行为。对三种不同类型的跟车行为进行建模，并优化计算关系以减少模型调整所需的参数数量。此外，我们改进了智能驾驶员模型（IDM）中动态期望间距的计算，以更好地适应实际道路交通状况。然后通过模拟复制交通流变化对改进后的模型进行校准。校准结果表明，我们的新模型在提高平均交通流速度和车辆速度稳定性方面具有显著优势。与经典跟车模型IDM相比，我们提出的模型使道路通行能力提高了8.9%。这些发现凸显了其在未来智能交通系统中广泛应用的潜力。本研究优化了跟车模型的理论框架，为提高高速交通系统效率提供了有力的技术支持。
