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Citation |
Alrejjal A, Moomen M, Ksaibati K. J. Traffic Transp. Eng. Engl. Ed. 2022; 9(4): 654-665. |
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Copyright |
(Copyright © 2022, Periodical Offices of Chang'an University, Publisher Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
This study investigated the impact of traffic violations on crash injury severity on Wyoming's interstate highways. A random parameters multinomial logit (MNL) model with heterogeneity in means was estimated as an alternative to the mixed logit model. This was done to better account for unobserved heterogeneity in the crash data. As per the results, the random parameters model with heterogeneity in means not only exhibited a better fit but also uncovered more insights regarding the factors influencing crash injury severity. The advanced model showed that traffic violations, crash characteristics and environmental characteristics among other factors impact crash injury severity on Wyoming's interstate highways. With regards to traffic violations, driving too fast for prevailing conditions and driving under the influence of alcohol and drugs were identified as the main violations that significantly influenced crash severity. Among other useful insights, the heterogeneity in mean specification indicated that the likelihood of severe injury crashes is increased by the interactive effect between non-trucks (vehicles not classified as trucks) and driving too fast for conditions. This is a significant implication that high speed behavior by non-truck drivers in adverse weather conditions is ranked as one of the hazardous traffic violations on Wyoming's interstates. This study provided for the first time important information on the impact of traffic violations on crash severity of crashes that occurred on challenging roadways that characterized by mountainous terrain and severe weather conditions. Language: en |
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Keywords |
Heterogeneity in means; Injury severity; Interstate safety; Random parameters |