以队列为中心的混合交通容量分析方法,包括联网车辆和自动驾驶车辆

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Peilin Zhao, Yiik Diew Wong, Feng Zhu
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引用次数: 0

摘要

混合交通容量的研究,包括连接和自动驾驶车辆(cav)和人类驾驶车辆(hv),仍然是一个关键的研究领域。传统模型通常关注单个车辆,而本研究将重点转移到队列作为分析的基本单位,以更好地捕捉自动驾驶汽车的队列特征。具体来说,我们引入了一个新的度量,即跨排队列强度(IPI),以方便混合交通容量的分析。通过数学和数值研究,我们评估了建议的IPI和最大排大小(MPS)对混合交通动力学的影响。研究结果表明:(1)IPI能够有效地衡量单车道混合交通环境下cav的聚类;(2)混合通行能力计算结果与实际通行能力基本吻合,偏差较小;(3)边际分析表明混合交通容量与MPS或IPI均单调相关的条件;(4)确定混合交通容量最大化的最优MPS。这些见解对现有关于队列大小和队列强度对混合交通流影响的文献有重要贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A platoon-centric approach to the capacity analysis of mixed traffic comprising connected and autonomous vehicles
The study of mixed traffic capacity, involving both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), remains a critical area of research. Traditional models have typically focused on individual vehicles, while this research shifts the focus to platoons as the fundamental units of analysis to better capture the platooning characteristics of CAVs. Specifically, we introduce a new metric, the Inter-Platoon Platooning Intensity (IPI), to facilitate the analysis of mixed traffic capacity. Through both mathematical and numerical investigations, we evaluate the impact of the proposed IPI and Maximum Platoon Size (MPS) on mixed traffic dynamics. Our findings indicate that: (1) the IPI effectively measures the clustering of CAVs in a single-lane mixed traffic environment; (2) the calculated mixed traffic capacity closely matches the actual traffic capacity, showing only minor deviations; (3) the marginal analysis demonstrates the conditions under which mixed traffic capacity correlates monotonically with either MPS or IPI; and (4) an optimal MPS is determined that maximizes mixed traffic capacity. These insights contribute significantly to the existing literature on the effects of platoon size and platooning intensity on mixed traffic flow.
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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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