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高速公路上移动瓶颈导致的平均行程延误评估。

Evaluation of average travel delay caused by moving bottlenecks on highways.

作者信息

Wei Xueyan, Xu Chengcheng, Wang Wei, Yang Menglin, Ren Xiaoma

机构信息

Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, Jiangsu, China.

Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, Jiangsu, China.

出版信息

PLoS One. 2017 Aug 30;12(8):e0183442. doi: 10.1371/journal.pone.0183442. eCollection 2017.

Abstract

This paper presents a modelling framework to evaluate travel delay of all vehicles influenced by moving bottlenecks on highways. During the derivation of analytical formulas, the arrival of slow vehicles was approximated by a Poisson process based on the assumption that they occupied a constant low proportion of the traffic stream. The mathematical analysis process was developed from moving bottlenecks with the same velocity to those with multiple different velocities, and the closed-form expression of expected average travel delay was obtained by utilizing kinematic-wave moving bottleneck theory, gap acceptance theory, probability theory and renewal theory. Model validation and parameters sensitive analysis were conducted by simulation relying on the open source database of US highway 10. The maximum passing rate and the macroscopic parameters of initial traffic state with maximum delay could be found by means of approximate formulas. The proposed modeling framework can be applied for evaluating impacts of slow vehicles on highway operation quantifiably, based on which traffic managements like truck prohibited period decision and speed or lane restriction could be made more scientifically.

摘要

本文提出了一个建模框架,用于评估高速公路上受移动瓶颈影响的所有车辆的行程延误。在推导解析公式的过程中,基于慢速车辆在交通流中所占比例恒定较低这一假设,通过泊松过程来近似慢速车辆的到达。数学分析过程从相同速度的移动瓶颈发展到具有多种不同速度的移动瓶颈,并利用运动波移动瓶颈理论、间隙接受理论、概率论和更新理论得到了预期平均行程延误的封闭形式表达式。通过基于美国10号高速公路开源数据库的模拟进行模型验证和参数敏感性分析。借助近似公式可以找到最大通过率和具有最大延误的初始交通状态的宏观参数。所提出的建模框架可用于定量评估慢速车辆对高速公路运营的影响,在此基础上,可以更科学地做出诸如卡车禁行时段决策以及速度或车道限制等交通管理措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc84/5576711/ede886d24aa0/pone.0183442.g001.jpg

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