The purpose of this research is to provide a faster and more efficient method to determine traffic density behavior for long-term congestion management using minimal statistical information. Applications include road work, road improvements, and route choice. To this end, this paper adapts and generalizes two analytical models (for non-peak and peak hours) for the probability mass function of traffic density for a major highway. It then validates the model against real data. The studied corridor has a total of 36 sensors, 18 in each direction, and the traffic experiences randomly occurring service deterioration due to accidents and inclement weather such as snow and thunderstorms. We base the models on queuing theory, and we compare the fundamental diagram with the data. This paper supports the validity of the models for each traffic condition under certain assumptions on the distributional properties of the associated random parameters. It discusses why these assumptions are needed and how they are determined. Furthermore, once the models are validated, different scenarios are presented to demonstrate traffic congestion behavior under various deterioration levels, as well as the estimation of traffic breakdown. These models, which account for non-recurrent congestion, can improve decision making without the need for extensive datasets or time-consuming simulations.

中文翻译：

---

发生事故的走廊上的交通密度：长期拥堵管理模型

这项研究的目的是提供一种更快和更有效的方法，以最少的统计信息来确定长期拥塞管理的流量密度行为。应用程序包括道路工程，道路改良和路线选择。为此，本文针对主要公路交通密度的概率质量函数，对两种分析模型（非高峰时间和高峰时间）进行了调整和归纳。然后根据真实数据验证模型。所研究的走廊共有36个传感器，每个方向上有18个，由于事故和恶劣天气（如雪和雷暴雨），交通会随机发生服务质量下降。我们基于排队论建立模型，并将基本图与数据进行比较。本文在有关随机参数的分布特性的某些假设下，支持每种交通状况模型的有效性。它讨论了为什么需要这些假设以及如何确定它们。此外，一旦模型得到验证，就会出现不同的场景，以展示在各种恶化水平下的交通拥堵行为，以及交通故障的估计。这些模型解决了非经常性的拥塞问题，无需大量数据集或耗时的模拟，即可改善决策制定。提出了不同的场景，以说明在各种恶化级别下的交通拥堵行为，以及交通中断的估计。这些模型解决了非经常性的拥塞问题，无需大量数据集或耗时的模拟，即可改善决策制定。提出了不同的场景，以说明在各种恶化级别下的交通拥堵行为，以及交通中断的估计。这些模型解决了非经常性的拥塞问题，无需大量数据集或耗时的模拟，即可改善决策制定。