评估高速公路破坏和恢复:一个随机模型

P. J. Ossenbruggen
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引用次数: 5

摘要

摘要采用二维流量和速度随机微分方程(SDE)模型对高速公路瓶颈状况进行了预测。随机行为被假定为标准布朗运动。为了充分描述现场观测条件,有必要将SDE模型与随机容量模型耦合,形成随机容量-微分方程(SCDE)模型。该模型包含以下机制:(1)当交通流量达到或超过高速公路容量时触发故障事件;(2)识别何时发生恢复。高速公路性能的测量是由SCDE模型预测得出的。它们包括故障概率(表示为交通流量和时间的函数)和恢复时间(即高速公路在恢复到自由流动状态之前保持拥堵状态的时间长度)。为了获得可靠的模型预测,在拟合随机容量模型时必须施加约束。约束条件包括…
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing Freeway Breakdown and Recovery: A Stochastic Model
AbstractConditions at a freeway bottleneck are predicted with a two-dimensional stochastic differential equation (SDE) model of flow and speed. Stochastic behavior is assumed to be standard Brownian motion. To fully describe conditions observed in the field, it is necessary to couple the SDE model with a stochastic capacity model, thus forming a stochastic capacity-differential equation (SCDE) model. The model contains mechanisms to (1) trigger a breakdown event when the traffic flow reaches or exceeds the freeway capacity, and (2) identify when recovery takes place. Measures of freeway performance are derived from SCDE model predictions. They include breakdown probability, expressed as a function of traffic flow and time-of-day, and recovery time, i.e., the length of time the freeway remains in a congested state before returning to a free-flow state. To achieve dependable model forecasts, it is necessary to impose constraints when fitting the stochastic capacity model. The constraint condition consists o...
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