协作控制框架:在信号灯交叉路口实现紧急车辆优先,同时尽量减少对普通车辆的负面影响

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-03-20 DOI:10.1155/2024/9691419
Ke-jun Long, Zhong-gen Zhang, Zhi-bo Gao, Si-qi Zhong, Jian Gu
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引用次数: 0

摘要

当一辆紧急车辆(EV)通过一个孤立的信号交叉口时,关键是要确保该车辆的高效通过,同时最大限度地减少对普通车辆(OV)的负面影响,尤其是在高车流量情况下。鉴于交叉口区域内时空资源的限制,电动汽车前方的 OV 在寻找安全空隙让行时往往面临挑战,从而对 OV 造成严重阻碍。本研究引入了一个新颖的协同控制框架,以联合优化动态应急车道设置和信号方案,同时考虑电动汽车优先权和单个信号灯交叉路口的 OVs 利益。首先,我们提出了一种动态应急车道控制算法,通过扩展和重新分配车辆的时间和空间资源来帮助路段中受阻的电动汽车。然后,我们建立了一个考虑电动汽车优先权和 OVs 利益的协同控制模型。该模型将最高优先权分配给紧急优先阶段,并优化信号方案,以防止相间冲突,同时考虑到 OV 的利益。最后,我们的协同控制框架还采用了生态驾驶算法来优化 OV 速度,以降低油耗。案例研究结果表明,与其他基线方法相比,我们提出的模型能显著减少电动汽车的行驶时间,同时降低 OV 的行驶时间和油耗。对不同交通流量场景的敏感性分析表明,随着车辆数量的增加,我们提出的方法能更明显地减少电动车和其他车辆的行驶时间。此外,动态应急车道的使用比例也在逐渐增加,并在更早的位置启动。
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A Collaborative Control Framework: Achieving Emergency Vehicle Priority While Minimizing Negative Impact on Ordinary Vehicles at Signalized Intersection

When an emergency vehicle (EV) passes through an isolated signal intersection, it is crucial to ensure the efficient passage of the EV while minimizing the negative impact on ordinary vehicles (OVs), particularly in high-traffic flow scenarios. Given the constraints on temporal and spatial resources within intersection areas, OVs ahead of EV often face challenges in finding safe gaps for giving way, resulting in significant obstructions to OVs. This research introduces a novel collaborative control framework to jointly optimize dynamic emergency lane settings and signal schemes, considering EV priority and OVs benefits for a single signalized intersection. Firstly, we propose a dynamic emergency lane control algorithm to help obstructed EV in roadway segments by extending and reallocating temporal and spatial resources for vehicles. Then, we establish a collaborative control model considering EV priority and OVs benefits. Assigning the highest priority to the emergency priority phase, this model optimizes signal schemes to prevent interphase conflict, taking into account OVs benefits. Finally, our collaborative control framework also employs an Eco-Driving algorithm for the optimization of OV speed to reduce fuel consumption. The case study results reveal that in comparison to other baseline methods, our proposed model significantly reduces EV travel time, simultaneously lowering the travel time and fuel consumption of OVs. Sensitivity analysis of varying traffic flow scenarios reveals that, as vehicle volumes increase, our proposed method demonstrates more pronounced reductions in both EV and OV travel time. In addition, there is a progressive increase in the proportion of dynamic emergency lane utilization, with activation occurring at earlier locations.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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