scholarly journals Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability

Author(s):  
Jiawei Wang ◽  
Yang Zheng ◽  
Chaoyi Chen ◽  
Qing Xu ◽  
Keqiang Li
Author(s):  
Jiawei Wang ◽  
Yang Zheng ◽  
Chaoyi Chen ◽  
Qing Xu ◽  
Keqiang Li

2021 ◽  
Vol 2025 (1) ◽  
pp. 012084
Author(s):  
Junjie Zhang ◽  
Can Yang ◽  
Haiyang Yu ◽  
Jun Zhang ◽  
Zixiao Wang

2020 ◽  
Vol 12 (21) ◽  
pp. 8941
Author(s):  
Yuntao Shi ◽  
Ye Li ◽  
Qing Cai ◽  
Hao Zhang ◽  
Dan Wu

Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiakuan Dong ◽  
Jiangfeng Wang ◽  
Lei Chen ◽  
Zhijun Gao ◽  
Dongyu Luo

With the emerging application of low-level driving automation technology, heterogeneous traffic flow mixed with human-driven vehicles and low-level autonomous vehicles is dawning. In this context, it is imperative to investigate its effect on mixed traffic flow. As a key component for adaptive cruise control (ACC) which is a practical low-level application of driving automation, the time gap policy determines the dynamic of ACC-equipped vehicles and plays a crucial role in traffic flow stability and efficiency. There are two main time gap policies used for ACC at present, namely, constant time gap (CTG) policy and variable time gap (VTG) policy. In this study, we carried out a detailed comparison between these time gap policies to investigate their potential effect on mixed traffic flow, where the analytical- and simulation-based approaches are both considered. Analytical results show that VTG policy is superior to CTG policy in stabilizing the mixed traffic flow. In addition, numerical simulations are also conducted and simulation results further support the analytical results. As for throughput, there is no difference between CTG policy and VTG policy in analytical progress when the same time gap is set at the equilibrium. However, simulation results based on an on-ramp scenario show that the throughput of mixed traffic flow with VTG policy is slightly higher than that of CTG policy. Meanwhile, the scatter of mixed traffic flow with VTG policy in the flow-density diagram gradually clusters in the middle range of density (i.e., 20–40 veh/km) with the increase of the penetration rates of ACC vehicles, where the traffic flow operates more efficiently. These results indicate that VTG policy is better than CTG policy when designing controllers for ACC in the context of traffic flow operation and control.


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