Department of Civil and Geological Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada.
Accid Anal Prev. 2011 May;43(3):1267-78. doi: 10.1016/j.aap.2011.01.009. Epub 2011 Feb 18.
During the last few decades, the two-fluid model and its two parameters have been widely used in transportation engineering to represent the quality of operational traffic service on urban arterials. Catastrophe models have also often been used to describe traffic flow on freeway sections. This paper demonstrates the possibility of developing a pro-active network screening tool that estimates the crash rate using a stochastic cusp catastrophe model with the two-fluid model's parameters as inputs. The paper investigates the analogy in logic behind the two-fluid model and the catastrophe model using straightforward graphical illustrations. The paper then demonstrates the application of two-fluid model parameters to a stochastic catastrophe model designed to estimate the level of safety on urban arterials. Current road safety management, including network safety screening, is post-active rather than pro-active in the sense that an existing hotspot must be identified before a safety improvement program can be implemented. This paper suggests that a stochastic catastrophe model can help us to become more pro-active by helping us to identify urban arterials that currently show an acceptable level of safety, but which are vulnerable to turning into crash hotspots. We would then be able to implement remedial actions before hotspots develop.
在过去的几十年中，双流体模型及其两个参数已被广泛应用于交通工程中，以表示城市干道上运营交通服务的质量。突变模型也经常被用于描述高速公路路段上的交通流。本文演示了开发主动网络筛选工具的可能性，该工具使用具有双流体模型参数作为输入的随机尖点突变模型来估计碰撞率。本文使用直观的图形说明研究了双流体模型和突变模型背后的逻辑相似性。然后，本文演示了将双流体模型参数应用于随机突变模型的方法，该模型旨在估计城市干道的安全水平。目前的道路安全管理，包括网络安全筛选，是后发性的，而不是前瞻性的，因为在实施安全改进计划之前，必须先确定现有的热点。本文提出，通过帮助我们识别当前显示出可接受安全水平但容易变成碰撞热点的城市干道，随机突变模型可以帮助我们变得更加前瞻性。然后，我们可以在热点发展之前采取补救措施。
