评估联网和自动驾驶车辆对涡轮环形交叉口运行性能影响的理论模型

IF 4.3 Q2 TRANSPORTATION
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引用次数: 0

摘要

本文介绍了一种方法,用于估算部分和完全互联与自动驾驶车辆(CAV)环境下基本涡轮环岛的入口通行能力(EC)和总通行能力(TC)。EC的计算部分基于HCM第七版提出的容量模型和调整系数,并考虑了CAV在交通流中的不同比例。所提出的方法被应用于一项案例研究,该案例涉及一个具有不同交通需求和 CAV 市场渗透水平(MPLs)的基本涡轮环岛。假设交通流由 100% 的乘用车组成,CAV 的 MPL 在 0% 到 100% 之间。研究证明,随着 CAV MPLs 的增加,ECs 也相应增加,延迟和排队现象减少。为了最大限度地提高交通流量,还假设了一个控制区域,在该区域内,CAV 开始与涡轮环岛管理系统进行通信。该系统应能分配和疏导 CAV,因此,入口车道之间的进入流量可在入口右侧车道和左侧车道之间找到机动分配系数(α、β、γ、δ)的值,以最大化每个交通流起点-终点矩阵的 TC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A theoretical model for evaluating the impact of connected and autonomous vehicles on the operational performance of turbo roundabouts

This article presents a methodology to estimate the entry capacity (EC) and total capacity (TC) of basic turbo roundabouts under partial and fully connected and autonomous vehicle (CAV) environments. EC calculations are partially based on capacity models and adjustment factors proposed by the HCM 7th edition, taking into account different proportions of CAVs in traffic streams. The proposed methodology was applied to a case study concerning a basic turbo roundabout with different traffic demands and market penetration levels (MPLs) of CAVs. It was assumed that the traffic stream consisted of 100% passenger cars with MPLs of CAVs ranging from 0% to 100%. The research proves that with the increase in MPLs of CAVs, ECs increase accordingly and delays and queues decrease. To maximize the TC, a control area was also hypothesized, where CAVs start to communicate with a turbo roundabout manager system. The system should be able to distribute and channel CAVs, and therefore the entering flows between entry lanes find the values of the maneuver distribution factors (α, β, γ, δ) between the right lane and the left lane of entries to maximize the TC for each origin–destination matrix of traffic flows.

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来源期刊
International Journal of Transportation Science and Technology
International Journal of Transportation Science and Technology Engineering-Civil and Structural Engineering
CiteScore
7.20
自引率
0.00%
发文量
105
审稿时长
88 days
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