The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.

中文翻译：

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不同跟车模型定量评估自适应巡航控制系统车头时距对拥堵城市高速公路的影响及早期控制结果


近年来，驾驶自动化和自适应巡航控制（ACC）对交通性能的影响得到了越来越多的研究。本文重点关注两种广泛使用的 ACC 跟车模型，并研究了加拿大安大略省最繁忙的高速公路走廊之一的车头时距参数对交通运行和性能的影响。利用Aimsun微仿真，我们比较了两种常用的ACC跟车模型；智能驾驶员模型（IDM）和最近在 Aimsun Next 20 中采用的 Shladover 模型。我们进行了多项实验来评估不同所需车头时距设置的高速公路性能以及配备 ACC 的车辆的市场渗透率。模拟结果证实了所报告的 IDM 缺点，即响应缓慢，导致车头时距误差，而 Shladover 模型的这种缺点不太明显，从而导致后者的量化更加准确。本研究进一步提出了一种基于开关 ACC 的简单交通控制策略，旨在使配备 ACC 的车辆的驾驶行为实时适应当前的交通状况，从而提高高速公路性能。仿真结果表明，即使对于 ACC 车辆的渗透率较低，与仅手动驾驶或不受控制的 ACC 车辆的情况相比，所提出的控制概念也提高了平均网络吞吐量、延迟和速度。