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  • A Crash Surrogate Metric considering Traffic Flow Dynamics in a Motorway Corridor

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    Wang179909.pdf (1.132Mb)
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    Author(s)
    Wang, Xu
    Liu, Kai
    Griffith University Author(s)
    Wang, Xu
    Year published
    2018
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    Abstract
    We proposed a new crash surrogate metric, i.e., the maximum disturbance that a car following scenario can accommodate, to represent potential crash risks with a simple closed form. The metric is developed in consideration of traffic flow dynamics. Then, we compared its performance in predicting the rear-end crash risks for motorway on-ramps with other two surrogate measures (time to collision and aggregated crash index). To this end, a one-lane on-ramp of Pacific Motorway, Australia, was selected for this case study. Due to the lack of crash data on the study site, historical crash counts were merged according to levels of ...
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    We proposed a new crash surrogate metric, i.e., the maximum disturbance that a car following scenario can accommodate, to represent potential crash risks with a simple closed form. The metric is developed in consideration of traffic flow dynamics. Then, we compared its performance in predicting the rear-end crash risks for motorway on-ramps with other two surrogate measures (time to collision and aggregated crash index). To this end, a one-lane on-ramp of Pacific Motorway, Australia, was selected for this case study. Due to the lack of crash data on the study site, historical crash counts were merged according to levels of service (LOS) and then converted into crash rates. In this study, we used the societal risk index to represent the crash surrogate indicators and built relationships with crash rates. The final results show that the proposed metric and aggregated crash index are superior to the time to collision in predicting the rear-end crash risks for on-ramps; they have a relatively similar performance, but due to the simple calculation, the proposed metric is more applicable to some real-world cases compared with the aggregated crash index.
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    Journal Title
    Journal of Advanced Transportation
    DOI
    https://doi.org/10.1155/2018/9349418
    Copyright Statement
    © 2018 Xu Wang and Kai Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Applied mathematics
    Civil engineering
    Publication URI
    http://hdl.handle.net/10072/383677
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    • Journal articles

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