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  • Estimation of rear-end vehicle crash frequencies in urban road tunnels

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    Author(s)
    Meng, Qiang
    Qu, Xiaobo
    Griffith University Author(s)
    Qu, Xiaobo
    Year published
    2012
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    Abstract
    According to The Handbook of Tunnel Fire Safety, over 90% (55 out of 61 cases) of fires in road tunnels are caused by vehicle crashes (especially rear-end crashes). It is thus important to develop a proper methodology that is able to estimate the rear-end vehicle crash frequency in road tunnels. In this paper, we first analyze the time to collision (TTC) data collected from two road tunnels of Singapore and conclude that Inverse Gaussian distribution is the best-fitted distribution to the TTC data. An Inverse Gaussian regression model is hence used to establish the relationship between the TTC and its contributing factors. ...
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    According to The Handbook of Tunnel Fire Safety, over 90% (55 out of 61 cases) of fires in road tunnels are caused by vehicle crashes (especially rear-end crashes). It is thus important to develop a proper methodology that is able to estimate the rear-end vehicle crash frequency in road tunnels. In this paper, we first analyze the time to collision (TTC) data collected from two road tunnels of Singapore and conclude that Inverse Gaussian distribution is the best-fitted distribution to the TTC data. An Inverse Gaussian regression model is hence used to establish the relationship between the TTC and its contributing factors. We then proceed to introduce a new concept of exposure to traffic conflicts as the mean sojourn time in a given time period that vehicles are exposed to dangerous scenarios, namely, the TTC is lower than a predetermined threshold value. We further establish the relationship between the proposed exposure to traffic conflicts and crash count by using negative binomial regression models. Based on the limited data samples used in this study, the negative binomial regression models perform well although a further study using more data is needed.
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    Journal Title
    Accident Analysis and Prevention
    Volume
    48
    DOI
    https://doi.org/10.1016/j.aap.2012.01.025
    Copyright Statement
    © 2012 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Transport engineering
    Transportation, logistics and supply chains
    Publication URI
    http://hdl.handle.net/10072/47097
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    • Journal articles

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