Examination of driver injury severity in freeway single-vehicle crashes using a mixed logit model with heterogeneity-in-means

The study presented in this paper thoroughly investigated factors influencing driver injury severity in freeway single-vehicle crashes. Crash data from 2013 to 2017 for freeways in Heilongjiang Province, China was used. Elements of driver characteristics, environmental factors, roadway attributes and crash characteristics were considered. A heterogeneity-in-means mixed logit model was developed as an alternative to the frequently used multinomial logit model and mixed logit model to fully account for unobserved heterogeneity, particularly the heterogeneity resulting from driver characteristics. Results indicated that the mixed logit model with heterogeneity-in-means can provide a superior goodness-of-fit and offer more insights into factors of driver injury severities. By allowing means of random parameters in mixed logit model to be estimated functions of driver characteristics, a more general model structure for deeply tracking unobserved heterogeneity was constructed, and thereby the interactive effects between driver characteristics and other factors on driver injury severity were uncovered, such as: (1) female and senior drivers, darkness without lighting, collision with barriers or piers, lane-changing or merging maneuvers tend to increase the injury severity of drivers; (2) an experienced driver was associated with low probability of severe injuries: (3) low visibility could reduce injury severity, especially for experienced drivers; (4) a concrete barrier could aggravate the injury severity for senior drivers in particular. This study provided an insightful knowledge of mechanism of driver injury severity in single-vehicle crashes, and should be beneficial to develop corresponding effective countermeasures for protect drivers from being severely injured. (C) 2019 Elsevier B.V. All rights reserved.