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Citation |
Chen S, Chen Y, Xing Y. J. Transp. Saf. Secur. 2022; 14(2): 280-304. |
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Copyright |
(Copyright © 2022, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group) |
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DOI |
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PMID |
unavailable |
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Abstract |
Underground road systems are becoming popular in cites because they can overcome urban space constraints and increase capacity and accessibility for urban transport systems. In cities with rivers and seas, the construction of cross-river tunnels can preserve land resources and reduce traffic congestion without affecting navigation. However, tunnel traffic safety has become an increasing concern due to frequent and serious tunnel traffic crashes. The severity of crashes and the difficulty of rescue in tunnels are higher than those of other road sections. To improve the safety of tunnel operation, this paper analyzes the crash data of 14 river-crossing tunnels in Shanghai from 2015 to 2016. A negative binomial (NB) model is employed for crash frequency, and a tobit model is employed for crash rate. With respect to possible spatial and temporal correlations in accident data and unobserved heterogeneity across observations, random-effect (RE) and random-parameter (RP) regression models are utilized. The tunnel geometry characteristics, traffic conditions and crash location are considered as independent variables. The results of the crash frequency show that annual average daily traffic (AADT), speed limit, grade, grade differences and the ratio of the vertical grade to the curve radius (RGR) are likely to increase the crash frequency in cross-river tunnels while horizontal curve radius, vertical curve radius and long tunnel are associated with fewer crashes. This study also explored the use of crash rate instead of crash frequency as a dependent variable by using a random-parameter tobit model. The results indicate that the significance of most independent variables is consistent with the results obtained from the random-parameter negative binomial model based on crash frequency. Language: en |
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Keywords |
crash frequency; crash rate; cross-river tunnel; negative binomial regression; random-effect model; random-parameter model; tobit regression |