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Assessment of Level-Of-Service for Freeway Segments Using HCM and Microsimulation Methods

机译:使用HCM和微观仿真方法评估高速公路路段的服务水平

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The Highway Capacity Manual (HCM) 2015 freeway facilities methodology offers a supplemental computational engine FREEVAL, which is a macroscopic/mesoscopic tool that enables users to implement HCM-based freeway analysis quickly and conveniently. On the other hand, Vissim is a microscopic simulation tool that enables users to model real-world conditions with high level of accuracy and comprehensiveness. Thus, the two tools represent quite opposite sides in freeway modelling – Vissim requires time-consuming preparation and calibration of the model but it usually provides benefits that are more comprehensive. FREEVAL requires less on input and calibration sides but its results may not be as beneficial as comprehensive and accurate as Vissim’s. The problem, that has not been addressed enough, is that we do not know how different their results are (when compared between themselves) and, at the same time, how close to the field conditions. Researchers and practitioners use both tools for freeway analysis and tend to compare the results directly. One of the commonly used performance measures is the Level of Service (LOS), which is used to quickly evaluate the freeway segment or facility performance. The HCM Freeway Facility Methodology uses density to estimate LOS. However, density is calculated differently in FREEVAL and Vissim, and comparing the estimated LOSs between the two may not represent a proper comparison. In essence, FREEVAL, in the under-saturated condition, estimates the density from the fundamental equations where the volume is estimated from the user entered demand and the speed is calculated using the statistical models provided in respective chapters of each segment type. On the other hand, Vissim tracks each individual vehicle as it moves along a freeway and calculates key performance measures by using individually modeled driver’s behavior. This paper aims to compare and contrast the methodologies behind the two tools and offer explanation and discussion of their outputs. The paper will cover four major HCM freeway segment types (basic, merge, diverge, and weaving) in under-saturated conditions. Field data will be acquired from a section of I-880 freeway in California. FREEVAL and Vissim models will be calibrated and validated using Mobile Century Data provided by University of California at Berkley and Caltrans Performance Measurement System. The output of both tools will be evaluated against the field data. The assessment should reveal the ability of each tool to replicate the real-world conditions. Paper results will contribute to the existing body of knowledge by filling the gap in the literature related to comparison and contrast of the key (LOS-related) performance measures of these two tools.
机译:《 2015年高速公路通行能力手册》(HCM)高速公路设施方法提供了补充的计算引擎FREEVAL,这是一种宏观/介观的工具,使用户能够快速便捷地实施基于HCM的高速公路分析。另一方面,Vissim是一种微观仿真工具,使用户能够以高水平的准确性和全面性对现实环境进行建模。因此,这两个工具在高速公路建模中代表了截然相反的方面– Vissim需要耗时的模型准备和校准,但通常会带来更全面的收益。 FREEVAL在输入和校准方面要求较少,但其结果可能不如Vissim的全面和准确。问题还没有得到足够的解决,就是我们不知道它们的结果有多大不同(当相互比较时),同时又不知道与现场条件有多接近。研究人员和从业人员使用这两种工具进行高速公路分析,并倾向于直接比较结果。服务水平(LOS)是最常用的绩效指标之一,用于快速评估高速公路路段或设施的绩效。 HCM高速公路设施方法论使用密度来估计LOS。但是,在FREEVAL和Vissim中,密度的计算方式有所不同,并且将两者之间的估计LOS进行比较可能并不代表正确的比较。本质上,在欠饱和状态下,FREEVAL根据基本方程式估算密度,其中,根据用户输入的需求估算体积,并使用每种分段类型的相应章节中提供的统计模型计算速度。另一方面,Vissim会跟踪每辆汽车在高速公路上行驶时的情况,并通过使用单独建模的驾驶员行为来计算关键性能指标。本文旨在比较和对比这两种工具背后的方法,并对它们的输出进行解释和讨论。本文将介绍饱和条件下的四种主要的HCM高速公路路段类型(基本,合并,发散和编织)。现场数据将从加利福尼亚的I-880高速公路的一部分中获取。 FREEVAL和Vissim模型将使用加州大学伯克利分校提供的Mobile Century Data和Caltrans Performance Measurement System进行校准和验证。两种工具的输出将根据现场数据进行评估。评估应揭示每种工具复制真实条件的能力。论文结果将填补现有文献中与这两种工具的关键(LOS相关)绩效指标的比较和对比有关的空白,从而有助于现有知识体系。

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