Characterizing Travel Time Variability in Vehicular Traffic Networks
Characterizing Travel Time Variability in Vehicular Traffic Networks
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DOI：
10.3141/2315-15
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发表时间：
2012-01
影响因子：
1.7
通讯作者：
H. Mahmassani;Tian Hou;Jing Dong
中科院分区：
文献类型：
--
作者：
H. Mahmassani;Tian Hou;Jing Dong
行程时间可靠性建模需要描述行程时间分布。通常用于描述分布的两个关键统计量是平均差和标准差，其中一个描述集中趋势，另一个描述分散。虽然平均行程时间比较容易测量和预测，但由于单个行程数据的不足，相应的标准差通常很难获得。基于赫尔曼和普里高津的动力学理论，本研究探讨了旅行时间变异性的一个强大的表征，提供了一个近线性的关系，每单位距离的旅行时间的标准差和相应的平均值。模拟生成的车辆轨迹数据从三个道路网络被用来探索这种关系。在多尺度和多水平分析的基础上，大量数据表明，这两个量是高度正相关的，即单位距离的个人旅行时间的分布的离散度增加的值的增加，单位距离的平均旅行时间。此外，回归模型和统计检验表明这种关系是线性或近线性的。这种关系也被来自华盛顿地区西雅图的全球定位系统探测数据所验证。这种关系提供了一个强大的基础，预测每单位距离的标准偏差时，平均值是已知的，因此，在战略和业务研究的网络中的旅行的可靠性的特点。
Modeling travel time reliability requires characterizing travel time distributions. Two key statistics commonly used to describe a distribution are the mean deviation and the standard deviation, with one depicting the central tendency and the other describing the dispersion. Although the mean travel time is easier to measure and predict, the corresponding standard deviation is usually hard to obtain because of the insufficiency of individual trip data. Building on seminal insight that goes back to Herman and Prigogine's kinetic theory, this study explores a robust characterization of travel time variability that provides for a near-linear relation between the standard deviation of travel time per unit distance and the corresponding mean value. Simulation-generated vehicle trajectory data from three road networks are used to explore this relationship. On the basis of multiscale and multilevel analysis, large amounts of data show that these two quantities are highly positively correlated; that is, the dispersion of the distribution of individual travel time per unit distance increases with increasing value of the mean travel time per unit distance. Furthermore, regression models and statistical testing indicate that this relation is linear or near-linear. The relation is also validated by Global Positioning System probe data from the Seattle, Washington, area. This relation provides a robust basis for predicting the standard deviation per unit distance when the mean value is known and thus for characterizing the reliability of travel in a network in strategic and operational studies.