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Teenage and adult drivers demonstrate different characteristics in terms of significant crash factors, collision risks, and injury severities on freeways. It is well recognized that reducing the number of freeway crashes involving teenage drivers, the most dangerous driver group, is of practical importance. A substantial understanding of teenage crash characteristics, injury severities, and significant attributes is required to develop an effective means of reducing teenage driver crash risks. In this study, the transportation econometric models, negative binomial regression models, were developed to analyze teenage and adult crashes using the data collected from Washington State freeways from 2004 through 2007. This research also explores the differences in crash injury severity between teenage and adult drivers. Three multivariate binary logit models of injury severity are established for the general driver group, the teenage driver group, and the adult driver group. The results indicate that although the majority of significant factors are similar for teenage and adult drivers, their slight difference is particularly useful in developing effective safety solutions addressing the teenage crashes. Additionally, a series of important behavioral differences between teenage and adult drivers are identified to facilitate the roadway design and injury-mitigation research. [ABSTRACT FROM AUTHOR]

• Driver injury severity in rear-end crashes among passenger cars and light trucks were examined. • Four crash configurations were considered based on leading and following vehicle types. • A latent class multinomial logit model with two distinctive classes were used. • Crash configurations are one of the key factors leading to different mechanisms relating to crash severity. Rear-end crash is a major type of traffic crashes leading to a large number of injuries and fatalities each year, and passenger cars and light trucks are two main vehicle types in rear-end crashes on US roadways. Passenger cars and light trucks are different in size, vehicle mass and driver's vision. It is necessary to investigate the driver injury outcome patterns in rear-end crashes between passenger cars and light trucks considering crash configurations regarding the leading and following vehicle types. This study employs latent class multinomial logit (MNL) model to examine the risk factors on driver injury severity along with heterogeneity in variable effects presented by the cluster pattern in two-vehicle rear-end crashes involving passenger cars and light trucks, considering four crash configuration types, i.e., a passenger car struck by a passenger car, a light truck struck by a light truck, a passenger car struck by a light truck, and a light truck struck by a passenger car as exploratory variables. A model with two latent classes, which indicates the heterogeneity in variable effects among all the observations, is found to best fit the 7-year crash dataset from Washington State. The pseudo-elasticities are calculated to quantify the marginal effects of the contributing factors. The risk factors curve and sloping road condition, driver without seatbelt, and driver age of 65 and above increase driver fatality and serious injury risk greatly, and these three factors contribute from different latent classes. The crash configuration of a passenger car struck by a light truck is found to be one of class characteristics factors, which indicates that the heterogeneity exists between these two vehicle types. This factor is also a risk factor of injury. Furthermore, the leading vehicle is found to be much more vulnerable and closely related to injury, especially when it is in the crash of a passenger car struck by a light truck. The latent classes discovered give theoretical evidence of how to appropriately select subset data for further model construction for practical interest of serious injury prevention. The risk factors and their influence on injury severity provide beneficial insights on developing relevant countermeasures and strategies for injury severity mitigation on rear-end crashes involving passenger cars and light trucks. [ABSTRACT FROM AUTHOR]