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

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. 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.