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Citation |
Dabbour E, Easa S, Haider M. Accid. Anal. Prev. 2017; 107: 20-30. |
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Affiliation |
Ted Rogers School of Management, Ryerson University, Toronto, Ontario M5B 2K3, Canada. Electronic address: murtaza.haider@ryerson.ca. |
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Copyright |
(Copyright © 2017, Elsevier Publishing) |
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DOI |
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PMID |
28755536 |
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Abstract |
This study attempts to identify significant factors that affect the severity of drivers' injuries when colliding with trains at railroad-grade crossings by analyzing the individual-specific heterogeneity related to those factors over a period of 15 years. Both fixed-parameter and random-parameter ordered regression models were used to analyze records of all vehicle-train collisions that occurred in the United States from January 1, 2001 to December 31, 2015. For fixed-parameter ordered models, both probit and negative log-log link functions were used. The latter function accounts for the fact that lower injury severity levels are more probable than higher ones. Separate models were developed for heavy and light-duty vehicles. Higher train and vehicle speeds, female, and young drivers (below the age of 21 years) were found to be consistently associated with higher severity of drivers' injuries for both heavy and light-duty vehicles. Furthermore, favorable weather, light-duty trucks (including pickup trucks, panel trucks, mini-vans, vans, and sports-utility vehicles), and senior drivers (above the age of 65 years) were found be consistently associated with higher severity of drivers' injuries for light-duty vehicles only. All other factors (e.g. air temperature, the type of warning devices, darkness conditions, and highway pavement type) were found to be temporally unstable, which may explain the conflicting findings of previous studies related to those factors. Language: en |
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Keywords |
Injury severity; Mixed ordered models; Negative log–log function; Ordinal regression; Railroad-grade crossings; Random-parameter ordered probit models; Temporal stability |