考虑微观车辆交互效应的通行能力下降：解析模型与高分辨率轨迹验证
Capacity drop accounting for microscopic vehicle interaction effects: analytical model and validation with high-resolution trajectories
AI总结：
提出一种解析方法估计排队车辆释放流量，通过量化犹豫车辆产生的空隙及其相互作用解释通行能力下降现象，并用数值模拟和真实轨迹验证。
摘要：
通行能力下降是指排队车辆释放流量低于理论基础设施容量的交通现象。本文提出一种通用的解析方法来估计高速公路交通的排队释放流量。通行能力下降主要归因于犹豫车辆，即随机且暂时进入加速延迟状态并在其前方产生空隙（即额外间距）的车辆。所提出的方法基于犹豫车辆的空间和时间位置分布以及相关延迟，估计所有犹豫车辆产生的预期总空隙长度。它还考虑了下游犹豫车辆触发的波动与上游犹豫车辆产生的空隙之间的相互作用。我们的分析表明，这种相互作用是先前研究中观察到的静止排队和拥堵波之间通行能力下降程度不同的关键机制。通过数值模拟和真实轨迹验证了模型的准确性。总体而言，所提出的方法提供了对通行能力下降的更深入理解，可用于交通流建模和控制。
英文摘要：
Capacity drop is a traffic phenomenon in which the discharge flow from a queue is lower than the theoretical infrastructure capacity. This paper proposes a generic analytical method to estimate the queue discharge flow of freeway traffic. Capacity drop is primarily attributed to hesitant vehicles, defined as vehicles that stochastically and temporarily enter an acceleration delay state and generate voids (i.e., extra gaps) in front of them. The proposed method estimates the expected total void length generated by all hesitant vehicles, based on the distributions of their spatial and temporal locations as well as the associated delays. It also accounts for interactions between the waves triggered by downstream hesitant vehicles and the voids generated by upstream ones. Our analysis reveals that this interaction is the key mechanism behind the differing extents of capacity drop observed between standing queues and jam waves in previous studies. The accuracy of the model is validated through both numerical simulations and real-world trajectories. Overall, the proposed method offers a deeper understanding of capacity drop, which can be leveraged in traffic flow modeling and control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
