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Estimating Freeway Travel Time Reliability for Traffic Operations and Planning

机译：为交通运营和规划估算高速公路出行时间的可靠性

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摘要

Travel time reliability (TTR) has attracted increasing attention in recent years, and is often listed as one of the major roadway performance and service quality measures for both traffic engineers and travelers. Measuring travel time reliability is the first step towards improving travel time reliability, ensuring on-time arrivals, and reducing travel costs. Four components may be primarily considered, including travel time estimation/collection, quantity of travel time selection, probability distribution selection, and TTR measure selection. Travel time is a key transportation performance measure because of its diverse applications and it also serves the foundation of estimating travel time reliability. Various modelling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a micro-simulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macro-scale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in St. Louis, Missouri was used to verify the estimated travel times produced by the proposed General Motors Travel Time Estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model outperformed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times. Most travel time reliability measures are derived directly from continuous probability distributions and applied to the traffic data directly. However, little previous research shows a consensus of probability distribution family selection for travel time reliability. Different probability distribution families could yield different values for the same travel time reliability measure (e.g. standard deviation). It is believe that the specific selection of probability distribution families has few effects on measuring travel time reliability. Therefore, two hypotheses are proposed in hope of accurately measuring travel time reliability. An experiment is designed to prove the two hypotheses. The first hypothesis is proven by conducting the Kolmogorov–Smirnov test and checking log-likelihoods, and Akaike information criterion with a correction for finite sample sizes (AICc) and Bayesian information criterion (BIC) convergences; and the second hypothesis is proven by examining both moment-based and percentile-based travel time reliability measures. The results from the two hypotheses testing suggest that 1) underfitting may cause disagreement in distribution selection, 2) travel time can be precisely fitted using mixture models with higher value of the number of mixture distributions (K), regardless of the distribution family, and 3) the travel time reliability measures are insensitive to the selection of distribution family. Findings of this research allows researchers and practitioners to avoid the work of testing various distributions, and travel time reliability can be more accurately measured using mixture models due to higher value of log-likelihoods. As with travel time collection, the accuracy of the observed travel time and the optimal travel time data quantity should be determined before using the TTR data. The statistical accuracy of TTR measures should be evaluated so that the statistical behavior and belief can be fully understood. More specifically, this issue can be formulated as a question: using a certain amount of travel time data, how accurate is the travel time reliability for a specific freeway corridor, time of day (TOD), and day of week (DOW)? A framework for answering this question has not been proposed in the past. Our study proposes a framework based on bootstrapping to evaluate the accuracy of TTR measures and answer the question. Bootstrapping is a computer-based method for assigning measures of accuracy to multiple types of statistical estimators without requiring a specific probability distribution. Three scenarios representing three traffic flow conditions (free-flow, congestion, and transition) were used to fully understand the accuracy of TTR measures under different traffic conditions. The results of the accuracy measurements primarily showed that: 1) the proposed framework can facilitate assessment of the accuracy of TTR, and 2) stabilization of the TTR measures did not necessarily correspond to statistical accuracy. The findings in our study also suggested that moment-based TTR measures may not be statistically sufficient for measuring freeway TTR. Additionally, our study suggested that 4 or 5 weeks of travel time data is enough for measuring freeway TTR under free-flow conditions, 40 weeks for congested conditions, and 35 weeks for transition conditions. A considerable number of studies have contributed to measuring travel time reliability. Travel time distribution estimation is considered as an important starting input of measuring travel time reliability. Kernel density estimation (KDE) is used to estimate travel time distribution, instead of parametric probability distributions, e.g. Lognormal distribution, the two state models. The Hasofer Lind - Rackwitz Fiessler (HL-RF) algorithm, widely used in the field of reliability engineering, is applied to this work. It is used to compute the reliability index of a system based on its previous performance. The computing procedure for travel time reliability of corridors on a freeway is first introduced. Network travel time reliability is developed afterwards. Given probability distributions estimated by the KDE technique, and an anticipated travel time from travelers, the two equations of the corridor and network travel time reliability can be used to address the question, "How reliable is my perceived travel time?" The definition of travel time reliability is in the sense of "on time performance", and it is conducted inherently from the perspective of travelers. Further, the major advantages of the proposed method are: 1) The proposed method demonstrates an alternative way to estimate travel time distributions when the choice of probability distribution family is still uncertain; 2) the proposed method shows its flexibility for being applied onto different levels of roadways (e.g. individual roadway segment or network). A user-defined anticipated travel time can be input, and travelers can utilize the computed travel time reliability information to plan their trips in advance, in order to better manage trip time, reduce cost, and avoid frustration.
机译：行驶时间可靠性（TTR）近年来受到越来越多的关注，并且经常被列为交通工程师和旅行者的主要道路性能和服务质量衡量指标之一。测量旅行时间可靠性是提高旅行时间可靠性，确保准时到达并降低旅行成本的第一步。可以主要考虑四个部分，包括旅行时间估计/收集，旅行时间选择的数量，概率分布选择和TTR度量选择。行程时间是一种重要的运输性能指标，因为其用途广泛，并且它是估算行程时间可靠性的基础。由于智能交通系统传感器的广泛安装，用于估算高速公路行驶时间的各种建模方法已经得到了很好的发展。但是，在拥挤的条件下，使用现有的高速公路出行时间模型估算准确的出行时间仍然是一项挑战。因此，本研究旨在开发一种基于通用汽车（GM）跟随模型的高速公路行驶时间估算模型。由于GM模型通常用于微仿真环境中，因此提出了虚拟引导和虚拟跟随车辆的概念，以允许GM模型在使用聚合交通传感器数据的宏观环境中使用。从密苏里州圣路易斯I-270的三个研究走廊收集的旅行时间数据用于验证拟议的通用汽车旅行时间估计（GMTTE）模型和两个现有模型（瞬时模型和时间）产生的估计旅行时间-切片模型。结果表明，由于自由流动条件下的平均平均误差较低，在两个拥挤条件下的平均误差为6.66％，GMTTE模型优于两个现有模型。总体而言，GMTTE模型证明了其在估算高速公路行驶时间方面的鲁棒性和准确性。大多数旅行时间可靠性度量直接从连续概率分布中得出，并直接应用于交通数据。然而，很少有先前的研究显示出对于旅行时间可靠性的概率分布族选择的共识。对于同一行进时间可靠性度量（例如标准差），不同的概率分布族可能会产生不同的值。可以相信，概率分布族的特定选择对测量旅行时间可靠性几乎没有影响。因此，提出了两个假设，希望能够准确地测量行程时间的可靠性。设计一个实验来证明这两个假设。通过进行Kolmogorov–Smirnov检验并检查对数似然性和Akaike信息标准，并通过对有限样本量（AICc）和贝叶斯信息标准（BIC）收敛进行了校正，证明了第一个假设。第二个假设是通过检查基于矩的旅行时间可靠性度量和基于百分位数的旅行时间可靠性度量来证明的。来自两个假设检验的结果表明：1）拟合不足可能会导致分布选择不一致； 2）可以使用具有较高混合物分布数量（K）值的混合物模型精确拟合行驶时间，而与分布族无关，并且3）行程时间可靠性措施对配电系列的选择不敏感。这项研究的结果使研究人员和从业人员可以避免测试各种分布的工作，并且由于对数似然值更高，因此使用混合模型可以更准确地测量出行时间可靠性。与旅行时间收集一样，应在使用TTR数据之前确定观察到的旅行时间的准确性和最佳旅行时间数据量。应该评估TTR度量的统计准确性，以便可以完全理解统计行为和信念。更具体地说，可以将这个问题表述为一个问题：使用一定数量的旅行时间数据，特定高速公路走廊，一天中的时间（TOD）和一周中的某天（DOW）的旅行时间可靠性有多准确？过去没有提出用于回答这个问题的框架。我们的研究提出了一个基于自举的框架，以评估TTR措施的准确性并回答问题。自举是一种基于计算机的方法，用于将准确性的度量分配给多种类型的统计估计量，而无需特定的概率分布。代表三种交通流状况（自由流，拥挤和过渡）的三种情况被用来全面了解不同交通状况下TTR措施的准确性。准确性测量结果主要表明：1）提出的框架可以促进对TTR准确性的评估。，以及2）TTR措施的稳定不一定与统计准确性相对应。我们的研究结果还表明，基于弯矩的TTR度量可能在统计上不足以测量高速公路的TTR。此外，我们的研究表明，在自由流动条件下，要测量高速公路的TTR，需要4或5周的旅行时间数据；对于拥挤的条件，则需要40周的时间；对于过渡条件，则需要35周的时间。大量的研究有助于测量旅行时间的可靠性。行程时间分布估计被认为是测量行程时间可靠性的重要开始​​输入。内核密度估计（KDE）用于估计旅行时间分布，而不是参数概率分布，例如对数正态分布，两种状态模型。在可靠性工程领域广泛使用的Hasofer Lind-Rackwitz Fiessler（HL-RF）算法被应用于这项工作。它用于根据系统以前的性能来计算系统的可靠性指标。首先介绍了高速公路走廊行进时间可靠性的计算过程。之后，网络旅行时间可靠性得到了发展。给定通过KDE技术估算的概率分布，以及来自旅行者的预期旅行时间，走廊和网络旅行时间可靠性的两个方程可用于解决以下问题：“我的感知旅行时间有多可靠？”旅行时间可靠性的定义是“按时表现”，它是从旅行者角度出发进行的。此外，该方法的主要优点是：1）当概率分布族的选择仍然不确定时，该方法演示了估计旅行时间分布的另一种方法； 2）所提出的方法显示了其灵活性，可应用于不同级别的道路（例如，各个道路段或网络）。可以输入用户定义的预期旅行时间，旅行者可以利用计算出的旅行时间可靠性信息来提前计划旅行，以便更好地管理旅行时间，降低成本并避免沮丧。

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    Yang Shu;

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  • 年度 2016
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