Quantitative Evaluation of the Impacts of the Time Headway of Adaptive Cruise Control Systems on Congested Urban Freeways Using Different Car Following Models and Early Control Results

Abstract

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.INDEX TERMS Adaptive cruise control systems, traffic control, traffic modeling.

Freeway Congestion Management With Reinforcement Learning Headway Control of Connected and Autonomous Vehicles

“…In our previous work, we demonstrated that in light traffic we could allow headways to be chosen by the driver, even if their choice was a long headway, because the impact on traffic was insignificant ( 5 ). However, in denser traffic, a simple on-off headway control strategy was demonstrated to improve freeway traffic performance significantly and transform the significant deterioration caused by adopting conservative headway settings into improvements by implementing the proposed control strategy ( 5 ).…”

“…According to ACC literature (2)(3)(4)(5), such systems have the potential to improve transportation network performance. However, if conservative values are set for ACC parameters (e.g., accelerations, headways) for comfort and safety reasons, deterioration in the transportation network performance is likely to happen.…”

“…Makridis et al ( 3 ) and Mattas et al ( 4 ) analyzed the effect of ACC users adopting conservative time headways on a real freeway stretch, showing that this will lead to significant deterioration in a network’s performance. In research by Elmorshedy et al, the effects of adopting different headway settings in the context of a long and congested urban freeway corridor with multiple bottlenecks and hotspots were investigated ( 5 ). The results of this study quantified that shorter time headway settings result in greater improvements, whereas adopting long headways leads to performance deterioration, which worsens as the penetration rate increases.…”

“…Headway control strategies can be implemented to reduce the deterioration caused by adopting conservative headway settings, as well as to increase the improvements that could be achieved by adopting shorter headways in case there is room for further improvement. We started this line of work by implementing a simple headway control strategy that varies the time headway according to the traffic situation ( 5 ). This has proven to be a promising solution to enhancing the performance of ACC systems and reducing the deterioration caused by their conservative behavior or, ideally, turning them into improvements.…”

“…For example, Liu et al studied the optimization of ACC system parameters, however, this study is based on an offline optimization technique that does not cope well with dynamically changing traffic conditions ( 6 ). Other studies address this limitation by adjusting the ACC system settings adaptively in real time according to dynamic traffic conditions (e.g., Elmorshedy et al [ 5 ], Kesting et al [ 7 ], and Spiliopoulou et al [ 8 , 9 ]). However, these latter studies mainly rely on historical observations in selecting the control parameters, which may not guarantee optimal behavior of these systems in real time.…”