Research highlights ► This study successfully applied macroscopic traffic flow parameters (i.e., two fluid model parameters) to represent the quality of urban arterial safety. ► This study introduced a stochastic cusp catastrophe model to estimate the crash rate of study arterials. ► The proposed method in this study can be viewed as a pro-active network screening method that can evaluate the latent hotspots.

    Abstract 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.


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    Title :

    A stochastic catastrophe model using two-fluid model parameters to investigate traffic safety on urban arterials


    Contributors:

    Published in:

    Publication date :

    2011-01-13


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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