工作区
测光模式
工作(物理)
流量(数学)
工作流程
流量(计算机网络)
计算机科学
环境科学
机械
分布式计算
工业工程
工程类
机械工程
计算机网络
物理
作者
Jun Liu,Qi-Lang Li,Ding-Jun Fu,Bing-Hong Wang
出处
期刊:International Journal of Modern Physics C
[World Scientific]
日期:2024-03-22
标识
DOI:10.1142/s0129183124501407
摘要
In the upstream of the highway work zone, vehicles will change lanes automatically or forcibly, which has complexity and randomness. Most studies fail to consider the impact of actual mixed traffic characteristics on traffic flow. For example, the dependence of the lane-changing behaviors of Connected and Automated Vehicles (CAVs) on the length of the zone is ignored in upstream of work zone. In addition, it is assumed that vehicles in the merge lane of the work zone would “yield” or not consider an impact on vehicles in the through lane. This simplifies the complex dynamics of the interaction between the two streams of traffic, which leads to the results of the study and the actual situation inconsistency. Based on the Gipps model, the adaptive cruise control (ACC) and the collaborative adaptive cruise control (CACC) vehicle-following models, this paper proposes a continuous cellular automata model under Vehicle-Road Collaboration and studies the mixed traffic flow characteristics in the work zone. By setting a metering zone before the lane reduction sign, ACC/CACC can adjust headway by obtaining road information in advance. The simulation results indicate that ACC/CACC vehicles have better following response compared to human-driven vehicles. In other words, the platoon composed of ACC/CACC vehicles has stability and can improve the traffic capacity of the work zone. The results of the study also found that the insertion of vehicles from adjacent lane into the ACC/CACC vehicles platoon is detrimental, especially when the penetration rate [Formula: see text] is high. Furthermore, when the length of the metering zone [Formula: see text] is set at 120–150[Formula: see text]m, it can significantly improve the road congestion in the work zone.
科研通智能强力驱动
Strongly Powered by AbleSci AI